diff --git a/setup_crewai_agents_google_colab.ipynb b/setup_crewai_agents_google_colab.ipynb index 6a8549e..e1173f9 100644 --- a/setup_crewai_agents_google_colab.ipynb +++ b/setup_crewai_agents_google_colab.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyMVql60VlNv2DbwP4b69lc6", + "authorship_tag": "ABX9TyODEXOKOlfyD42Q6UHAhHN8", "include_colab_link": true }, "kernelspec": { @@ -92,28 +92,28 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "Tptn3IAGl513", - "outputId": "982aa408-d78a-45e7-8a60-fdc2c0a0753b" + "outputId": "6c170613-bda4-4a85-f200-aff5594bd4c1" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ], "text/html": [ "\n", - "\n", + "
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Data Frame Summary
titanic
Dimensions: 891 x 12
Duplicates: 0
NoVariableStats / ValuesFreqs / (% of Valid)GraphMissingNoVariableStats / ValuesFreqs / (% of Valid)GraphMissing
1PassengerId
[int64]		Mean (sd) : 446.0 (257.4)
min < med < max:
1.0 < 446.0 < 891.0
IQR (CV) : 445.0 (1.7)		891 distinct values0
(0.0%)		1PassengerId
[int64]		Mean (sd) : 446.0 (257.4)
min < med < max:
1.0 < 446.0 < 891.0
IQR (CV) : 445.0 (1.7)		891 distinct values0
(0.0%)
2Survived
[int64]		1. 0
2. 1		549 (61.6%)
342 (38.4%)		0
(0.0%)		2Survived
[int64]		1. 0
2. 1		549 (61.6%)
342 (38.4%)		0
(0.0%)
3Pclass
[int64]		1. 3
2. 1
3. 2		491 (55.1%)
216 (24.2%)
184 (20.7%)		0
(0.0%)		3Pclass
[int64]		1. 3
2. 1
3. 2		491 (55.1%)
216 (24.2%)
184 (20.7%)		0
(0.0%)
4Name
[object]		1. Dooley, Mr. Patrick
2. Braund, Mr. Owen Harris
3. Cumings, Mrs. John Bradley (Fl
4. Heikkinen, Miss. Laina
5. Futrelle, Mrs. Jacques Heath (
6. Allen, Mr. William Henry
7. Moran, Mr. James
8. McCarthy, Mr. Timothy J
9. Palsson, Master. Gosta Leonard
10. Johnson, Mrs. Oscar W (Elisabe
11. other		1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
881 (98.9%)		0
(0.0%)		4Name
[object]		1. Dooley, Mr. Patrick
2. Braund, Mr. Owen Harris
3. Cumings, Mrs. John Bradley (Fl
4. Heikkinen, Miss. Laina
5. Futrelle, Mrs. Jacques Heath (
6. Allen, Mr. William Henry
7. Moran, Mr. James
8. McCarthy, Mr. Timothy J
9. Palsson, Master. Gosta Leonard
10. Johnson, Mrs. Oscar W (Elisabe
11. other		1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
881 (98.9%)		0
(0.0%)
5Sex
[object]		1. male
2. female		577 (64.8%)
314 (35.2%)		0
(0.0%)		5Sex
[object]		1. male
2. female		577 (64.8%)
314 (35.2%)		0
(0.0%)
6Age
[float64]		Mean (sd) : 29.7 (14.5)
min < med < max:
0.4 < 28.0 < 80.0
IQR (CV) : 17.9 (2.0)		88 distinct values177
(19.9%)		6Age
[float64]		Mean (sd) : 29.7 (14.5)
min < med < max:
0.4 < 28.0 < 80.0
IQR (CV) : 17.9 (2.0)		88 distinct values177
(19.9%)
7SibSp
[int64]		1. 0
2. 1
3. 2
4. 4
5. 3
6. 8
7. 5		608 (68.2%)
209 (23.5%)
28 (3.1%)
18 (2.0%)
16 (1.8%)
7 (0.8%)
5 (0.6%)		0
(0.0%)		7SibSp
[int64]		1. 0
2. 1
3. 2
4. 4
5. 3
6. 8
7. 5		608 (68.2%)
209 (23.5%)
28 (3.1%)
18 (2.0%)
16 (1.8%)
7 (0.8%)
5 (0.6%)		0
(0.0%)
8Parch
[int64]		1. 0
2. 1
3. 2
4. 5
5. 3
6. 4
7. 6		678 (76.1%)
118 (13.2%)
80 (9.0%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
1 (0.1%)		0
(0.0%)		8Parch
[int64]		1. 0
2. 1
3. 2
4. 5
5. 3
6. 4
7. 6		678 (76.1%)
118 (13.2%)
80 (9.0%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
1 (0.1%)		0
(0.0%)
9Ticket
[object]		1. 347082
2. 1601
3. CA. 2343
4. 3101295
5. CA 2144
6. 347088
7. 382652
8. S.O.C. 14879
9. 113760
10. 19950
11. other		7 (0.8%)
7 (0.8%)
7 (0.8%)
6 (0.7%)
6 (0.7%)
6 (0.7%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
4 (0.4%)
834 (93.6%)		0
(0.0%)		9Ticket
[object]		1. 347082
2. 1601
3. CA. 2343
4. 3101295
5. CA 2144
6. 347088
7. 382652
8. S.O.C. 14879
9. 113760
10. 19950
11. other		7 (0.8%)
7 (0.8%)
7 (0.8%)
6 (0.7%)
6 (0.7%)
6 (0.7%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
4 (0.4%)
834 (93.6%)		0
(0.0%)
10Fare
[float64]		Mean (sd) : 32.2 (49.7)
min < med < max:
0.0 < 14.5 < 512.3
IQR (CV) : 23.1 (0.6)		248 distinct values0
(0.0%)		10Fare
[float64]		Mean (sd) : 32.2 (49.7)
min < med < max:
0.0 < 14.5 < 512.3
IQR (CV) : 23.1 (0.6)		248 distinct values0
(0.0%)
11Cabin
[object]		1. nan
2. G6
3. C23 C25 C27
4. B96 B98
5. F2
6. D
7. E101
8. C22 C26
9. F33
10. C83
11. other		687 (77.1%)
4 (0.4%)
4 (0.4%)
4 (0.4%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
2 (0.2%)
175 (19.6%)		687
(77.1%)		11Cabin
[object]		1. nan
2. G6
3. C23 C25 C27
4. B96 B98
5. F2
6. D
7. E101
8. C22 C26
9. F33
10. C83
11. other		687 (77.1%)
4 (0.4%)
4 (0.4%)
4 (0.4%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
2 (0.2%)
175 (19.6%)		687
(77.1%)
12Embarked
[object]		1. S
2. C
3. Q
4. nan		644 (72.3%)
168 (18.9%)
77 (8.6%)
2 (0.2%)		2
(0.2%)		12Embarked
[object]		1. S
2. C
3. Q
4. nan		644 (72.3%)
168 (18.9%)
77 (8.6%)
2 (0.2%)		2
(0.2%)
\n" ] }, "metadata": {}, - "execution_count": 18 + "execution_count": 32 } ], "source": [ @@ -326,9 +326,9 @@ "base_uri": "https://localhost:8080/" }, "id": "Z4rjQE548hZ7", - "outputId": "8df3d7e5-4a7b-4c83-8ca4-8b0a73a3ffea" + "outputId": "fda66e4d-1a27-4a6c-ff5f-a339939c3a1c" }, - "execution_count": 19, + "execution_count": 33, "outputs": [ { "output_type": "stream", @@ -400,6 +400,24 @@ "execution_count": null, "outputs": [] }, + { + "cell_type": "markdown", + "source": [ + "## Create three agents that analyze the dataset" + ], + "metadata": { + "id": "FLk2Tisd1G0N" + } + }, + { + "cell_type": "markdown", + "source": [ + "We demonstrate how to automate the preliminary steps of understanding a new dataset, specifically the Titanic dataset, using the CrewAI framework and Google's Gemini Flash model. Instead of manual exploration, we orchestrate a team of specialized AI agents – a Data Quality Analyst, a Statistical Analyst, and a Predictive Modeling Advisor – to examine a comprehensive summary of the data. This method allows for a quick and insightful assessment of potential data quality issues like missing values. It highlights statistical anomalies such as outliers, and even suggests which features might be most valuable for future predictive modeling efforts, all without writing extensive exploratory code." + ], + "metadata": { + "id": "TQA6uFNt1nXy" + } + }, { "cell_type": "code", "source": [ @@ -410,6 +428,7 @@ "from summarytools import dfSummary\n", "from crewai import Agent, Task, Crew, LLM\n", "\n", + "clean_summary = remove_img_texts(df_summary_info.to_string())\n", "llm = LLM(model=\"gemini/gemini-2.0-flash\")\n", "\n", "# -----------------------------------------------------------------------------\n", @@ -463,7 +482,7 @@ "Focus primarily on those columns with more than 20% missing values, and consider columns with unusual uniqueness metrics.\n", "Provide detailed insights including the percentage of missing data and any recommendations for cleaning.\n", "Dataset Summary:\n", - "{df_summary_info}\"\"\",\n", + "{clean_summary}\"\"\",\n", " expected_output=\"A detailed list of column names with significant missing data issues along with insights and recommendations for data cleaning.\",\n", " agent=data_quality_agent\n", ")\n", @@ -475,7 +494,7 @@ "Specifically, look for columns where the maximum value far exceeds the mean (e.g., max > 3 × mean) and any unusual spread in the data.\n", "Include a brief explanation of why each identified column may be problematic. Provide any recommendations for further investigation.\n", "Dataset Summary:\n", - "{df_summary_info}\"\"\",\n", + "{clean_summary}\"\"\",\n", " expected_output=\"A detailed list of numeric columns with potential outlier issues, including a brief explanation for each.\",\n", " agent=statistical_agent\n", ")\n", @@ -485,7 +504,7 @@ " description=f\"\"\"Predictive Modeling Insight Task:\n", "Based on the dataset summary below, recommend which columns might be most informative for building a predictive model.\n", "Dataset Summary:\n", - "{df_summary_info}\"\"\",\n", + "{clean_summary}\"\"\",\n", " expected_output=\"A list of column names that are likely to be most informative for predictive modeling.\",\n", " agent=insight_agent\n", ")\n", @@ -513,9 +532,9 @@ "height": 1000 }, "id": "UDmAXJ6EhaCQ", - "outputId": "17e8af85-c294-41e1-a7bc-d220b0e60ac7" + "outputId": "1bdccf3f-9f70-4b1e-9e23-e4ea3a4bd77b" }, - "execution_count": 17, + "execution_count": 35, "outputs": [ { "output_type": "display_data", @@ -525,7 +544,7 @@ "\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n", "\u001b[36m│\u001b[0m \u001b[1;36mCrew Execution Started\u001b[0m \u001b[36m│\u001b[0m\n", "\u001b[36m│\u001b[0m \u001b[37mName: \u001b[0m\u001b[36mcrew\u001b[0m \u001b[36m│\u001b[0m\n", - "\u001b[36m│\u001b[0m \u001b[37mID: \u001b[0m\u001b[36mfb25122b-a197-4be3-98e2-7187488389ea\u001b[0m \u001b[36m│\u001b[0m\n", + "\u001b[36m│\u001b[0m \u001b[37mID: \u001b[0m\u001b[36m29f787f1-e6ba-4749-8b57-97e51a2c4aa5\u001b[0m \u001b[36m│\u001b[0m\n", "\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n", "\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n", "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" @@ -535,7 +554,7 @@ "│ │\n", "│ Crew Execution Started │\n", "│ Name: crew │\n", - "│ ID: fb25122b-a197-4be3-98e2-7187488389ea │\n", + "│ ID: 29f787f1-e6ba-4749-8b57-97e51a2c4aa5 │\n", "│ │\n", "│ │\n", "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n", @@ -562,12 +581,12 @@ "data": { "text/plain": [ "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n", - "└── \u001b[1;33m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n", + "└── \u001b[1;33m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n", " \u001b[37m Status: \u001b[0m\u001b[2;33mExecuting Task...\u001b[0m\n" ], "text/html": [ "
🚀 Crew: crew\n",
-              "└── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
+              "└── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
               "       Status: Executing Task...\n",
               "
\n" ] @@ -592,14 +611,14 @@ "data": { "text/plain": [ "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n", - "└── \u001b[1;33m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n", + "└── \u001b[1;33m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n", " \u001b[37m Status: \u001b[0m\u001b[2;33mExecuting Task...\u001b[0m\n", " └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n", " \u001b[37m Status: \u001b[0m\u001b[1;32mIn Progress\u001b[0m\n" ], "text/html": [ "
🚀 Crew: crew\n",
-              "└── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
+              "└── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
               "       Status: Executing Task...\n",
               "    └── 🤖 Agent: Data Quality Analyst\n",
               "            Status: In Progress\n",
@@ -632,61 +651,58 @@
             "Provide detailed insights including the percentage of missing data and any recommendations for cleaning.\n",
             "Dataset Summary:\n",
             "No Variable Stats / Values Freqs / (% of Valid) Graph Missing\n",
-            "1 PassengerId
[int64] Mean (sd) : 446.0 (257.4)
min < med < max:
1.0 < 446.0 < 891.0
IQR (CV) : 445.0 (1.7) 891 distinct values  0
(0.0%)\n",
-            "2 Survived
[int64] 1. 0
2. 1 549 (61.6%)
342 (38.4%)  0
(0.0%)\n",
-            "3 Pclass
[int64] 1. 3
2. 1
3. 2 491 (55.1%)
216 (24.2%)
184 (20.7%)  0
(0.0%)\n",
-            "4 Name
[object] 1. Dooley, Mr. Patrick
2. Braund, Mr. Owen Harris
3. Cumings, Mrs. John Bradley (Fl
4. Heikkinen, Miss. Laina
5. Futrelle, Mrs. Jacques Heath (
6. Allen, Mr. William Henry
7. Moran, Mr. James
8. McCarthy, Mr. Timothy J
9. Palsson, Master. Gosta Leonard
10. Johnson, Mrs. Oscar W (Elisabe
11. other 1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
881 (98.9%)  0
(0.0%)\n",
-            "5 Sex
[object] 1. male
2. female 577 (64.8%)
314 (35.2%)  0
(0.0%)\n",
-            "6 Age
[float64] Mean (sd) : 29.7 (14.5)
min < med < max:
0.4 < 28.0 < 80.0
IQR (CV) : 17.9 (2.0) 88 distinct values  177
(19.9%)\n",
-            "7 SibSp
[int64] 1. 0
2. 1
3. 2
4. 4
5. 3
6. 8
7. 5 608 (68.2%)
209 (23.5%)
28 (3.1%)
18 (2.0%)
16 (1.8%)
7 (0.8%)
5 (0.6%)  0
(0.0%)\n",
-            "8 Parch
[int64] 1. 0
2. 1
3. 2
4. 5
5. 3
6. 4
7. 6 678 (76.1%)
118 (13.2%)
80 (9.0%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
1 (0.1%)  0
(0.0%)\n",
-            "9 Ticket
[object] 1. 347082
2. 1601
3. CA. 2343
4. 3101295
5. CA 2144
6. 347088
7. 382652
8. S.O.C. 14879
9. 113760
10. 19950
11. other 7 (0.8%)
7 (0.8%)
7 (0.8%)
6 (0.7%)
6 (0.7%)
6 (0.7%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
4 (0.4%)
834 (93.6%)  0
(0.0%)\n",
-            "10 Fare
[float64] Mean (sd) : 32.2 (49.7)
min < med < max:
0.0 < 14.5 < 512.3
IQR (CV) : 23.1 (0.6) 248 distinct values  0
(0.0%)\n",
-            "11 Cabin
[object] 1. nan
2. G6
3. C23 C25 C27
4. B96 B98
5. F2
6. D
7. E101
8. C22 C26
9. F33
10. C83
11. other 687 (77.1%)
4 (0.4%)
4 (0.4%)
4 (0.4%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
2 (0.2%)
175 (19.6%)  687
(77.1%)\n",
-            "12 Embarked
[object] 1. S
2. C
3. Q
4. nan 644 (72.3%)
168 (18.9%)
77 (8.6%)
2 (0.2%)  2
(0.2%)\n",
+            "1 PassengerId
[int64] Mean (sd) : 446.0 (257.4)
min < med < max:
1.0 < 446.0 < 891.0
IQR (CV) : 445.0 (1.7) 891 distinct values  0
(0.0%)\n",
+            "2 Survived
[int64] 1. 0
2. 1 549 (61.6%)
342 (38.4%)  0
(0.0%)\n",
+            "3 Pclass
[int64] 1. 3
2. 1
3. 2 491 (55.1%)
216 (24.2%)
184 (20.7%)  0
(0.0%)\n",
+            "4 Name
[object] 1. Dooley, Mr. Patrick
2. Braund, Mr. Owen Harris
3. Cumings, Mrs. John Bradley (Fl
4. Heikkinen, Miss. Laina
5. Futrelle, Mrs. Jacques Heath (
6. Allen, Mr. William Henry
7. Moran, Mr. James
8. McCarthy, Mr. Timothy J
9. Palsson, Master. Gosta Leonard
10. Johnson, Mrs. Oscar W (Elisabe
11. other 1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
881 (98.9%)  0
(0.0%)\n",
+            "5 Sex
[object] 1. male
2. female 577 (64.8%)
314 (35.2%)  0
(0.0%)\n",
+            "6 Age
[float64] Mean (sd) : 29.7 (14.5)
min < med < max:
0.4 < 28.0 < 80.0
IQR (CV) : 17.9 (2.0) 88 distinct values  177
(19.9%)\n",
+            "7 SibSp
[int64] 1. 0
2. 1
3. 2
4. 4
5. 3
6. 8
7. 5 608 (68.2%)
209 (23.5%)
28 (3.1%)
18 (2.0%)
16 (1.8%)
7 (0.8%)
5 (0.6%)  0
(0.0%)\n",
+            "8 Parch
[int64] 1. 0
2. 1
3. 2
4. 5
5. 3
6. 4
7. 6 678 (76.1%)
118 (13.2%)
80 (9.0%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
1 (0.1%)  0
(0.0%)\n",
+            "9 Ticket
[object] 1. 347082
2. 1601
3. CA. 2343
4. 3101295
5. CA 2144
6. 347088
7. 382652
8. S.O.C. 14879
9. 113760
10. 19950
11. other 7 (0.8%)
7 (0.8%)
7 (0.8%)
6 (0.7%)
6 (0.7%)
6 (0.7%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
4 (0.4%)
834 (93.6%)  0
(0.0%)\n",
+            "10 Fare
[float64] Mean (sd) : 32.2 (49.7)
min < med < max:
0.0 < 14.5 < 512.3
IQR (CV) : 23.1 (0.6) 248 distinct values  0
(0.0%)\n",
+            "11 Cabin
[object] 1. nan
2. G6
3. C23 C25 C27
4. B96 B98
5. F2
6. D
7. E101
8. C22 C26
9. F33
10. C83
11. other 687 (77.1%)
4 (0.4%)
4 (0.4%)
4 (0.4%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
2 (0.2%)
175 (19.6%)  687
(77.1%)\n",
+            "12 Embarked
[object] 1. S
2. C
3. Q
4. nan 644 (72.3%)
168 (18.9%)
77 (8.6%)
2 (0.2%)  2
(0.2%)\n",
             "\u001b[00m\n",
             "\n",
             "\n",
             "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mData Quality Analyst\u001b[00m\n",
             "\u001b[95m## Final Answer:\u001b[00m \u001b[92m\n",
-            "**Detailed Data Quality Assessment:**\n",
+            "**Data Quality Analysis and Recommendations**\n",
+            "\n",
+            "Based on the provided dataset summary, the following columns exhibit data quality issues that warrant attention:\n",
             "\n",
             "1.  **Age:**\n",
             "\n",
-            "    *   **Missing Data:** 177 missing values, which is approximately 19.9% of the dataset.\n",
-            "    *   **Data Quality Issue:** While just below the 20% threshold, a significant number of 'Age' values are missing. This could introduce bias if not handled properly.\n",
-            "    *   **Recommendation:**\n",
-            "\n",
-            "        *   **Imputation:** Consider imputing missing 'Age' values using methods such as mean, median, or more advanced techniques like regression imputation based on other features (e.g., 'Pclass', 'Sex', 'SibSp', 'Parch').\n",
-            "        *   **Model-Based Imputation:** Use machine learning models to predict missing ages based on other features.\n",
-            "        *   **Consideration:** Be mindful of introducing bias during imputation; evaluate the impact of different imputation methods on downstream analyses.\n",
+            "*   **Missing Values:** 177 missing values (19.9%). While this is close to the 20% threshold, it's still a substantial amount of missing data that could bias analyses.\n",
+            "*   **Recommendation:**\n",
+            "    *   **Imputation:** Consider using imputation techniques to fill in the missing age values. Simple methods like mean or median imputation could be used, but more sophisticated methods like regression imputation or using machine learning models to predict age based on other features (e.g., Pclass, Sex, SibSp, Parch) might be more appropriate. The choice of imputation method should be guided by the nature of the data and the goals of the analysis.\n",
+            "    *   **Consider creating a new feature:** Create a new feature 'Age_Missing' that indicates whether the age was originally missing. This allows the model to capture any potential information encoded in the missingness itself.\n",
             "\n",
             "2.  **Cabin:**\n",
             "\n",
-            "    *   **Missing Data:** 687 missing values, which is a substantial 77.1% of the dataset.\n",
-            "    *   **Data Quality Issue:** The high percentage of missing 'Cabin' values makes this column unreliable for direct analysis without careful handling.\n",
-            "    *   **Recommendation:**\n",
-            "\n",
-            "        *   **Imputation with Caution:** Due to the high missingness, imputing cabin values might not be appropriate. Consider creating a new binary feature indicating whether a cabin is known ('Cabin_Known') or unknown ('Cabin_Unknown'). This preserves the information about whether the cabin was recorded.\n",
-            "        *   **Alternative Feature Engineering:** If cabin information is crucial, explore extracting relevant information from the existing 'Cabin' values before creating the 'Cabin_Known' feature, such as the deck level (e.g., first letter of the cabin).\n",
-            "        *   **Consider Deletion:** If cabin information proves unrecoverable or irrelevant to the analysis goals, consider removing the column.\n",
+            "*   **Missing Values:** 687 missing values (77.1%). This is a very high percentage of missing data.\n",
+            "*   **Recommendation:**\n",
+            "    *   **Drop the column:** Given the high percentage of missing values, the simplest approach would be to drop the column entirely. However, this might result in loss of potentially useful information.\n",
+            "    *   **Imputation with caution:** If you suspect the cabin information is important, you could attempt to impute the missing values. A common approach is to create a new category for missing values (e.g., \"Unknown\"). However, be aware that this could introduce bias if the missingness is not random.\n",
+            "    *   **Feature Engineering:** It might be more useful to extract the first letter of the cabin to represent the cabin type (e.g., A, B, C). Then, create a separate binary variable indicating whether the cabin information was missing. This would help to retain some information from the 'Cabin' column while addressing the large number of missing entries.\n",
+            "    *   **Investigate Missingness:** Determine if the missingness of the 'Cabin' column is related to other variables (e.g. Pclass). Perhaps passengers in certain classes were less likely to have cabin information recorded.\n",
             "\n",
             "3.  **Embarked:**\n",
             "\n",
-            "    *   **Missing Data:** 2 missing values, representing 0.2% of the dataset.\n",
-            "    *   **Data Quality Issue:** Very few missing values, but still needs to be addressed.\n",
-            "    *   **Recommendation:**\n",
+            "*   **Missing Values:** 2 missing values (0.2%). This is a small percentage of missing data.\n",
+            "*   **Recommendation:**\n",
+            "    *   **Imputation:** Since only 2 values are missing, simple imputation is feasible. You could impute with the most frequent value (mode), which is 'S' in this case. Alternatively, you could investigate the passengers with missing 'Embarked' values and try to determine their port of embarkation based on other information (e.g., ticket number, name).\n",
             "\n",
-            "        *   **Imputation:** Given the small number of missing values, impute using the mode (most frequent value). From the summary, 'S' appears to be the most frequent.\n",
-            "        *   **Investigation:** If possible, investigate the records with missing 'Embarked' values to determine if there is a logical basis for imputation.\n",
+            "**Summary Table**\n",
             "\n",
-            "**Summary of Recommendations:**\n",
+            "| Column     | Missing Values (%) | Recommendation                                                                                                                                                                                                                                        |\n",
+            "|------------|--------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
+            "| Age        | 19.9%              | Imputation (mean, median, regression, or ML-based). Consider creating a new 'Age_Missing' feature.                                                                                                                                                 |\n",
+            "| Cabin      | 77.1%              | Consider dropping the column, imputing with a new category (e.g., \"Unknown\"), or feature engineering to extract cabin type and create a missingness indicator. Investigate relationship between missingness and other features.                                                                                                                                                                                              |\n",
+            "| Embarked   | 0.2%               | Impute with the mode ('S') or investigate the specific passengers to determine their port of embarkation.                                                                                                                                       |\n",
             "\n",
-            "*   Address the missing 'Age' values through appropriate imputation techniques.\n",
-            "*   Handle the 'Cabin' column by creating a new binary feature to indicate the presence or absence of cabin information or by extracting useful information from existing cabin values.\n",
-            "*   Impute the missing 'Embarked' values using the mode.\n",
-            "\n",
-            "These steps will help improve the data quality and reliability for subsequent analysis and modeling.\u001b[00m\n",
+            "By addressing these data quality issues, you can improve the reliability and validity of your analyses and models. The specific cleaning approach should be chosen based on the context of the analysis and the potential impact on the results.\u001b[00m\n",
             "\n",
             "\n"
           ]
@@ -696,14 +712,14 @@
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-              "└── \u001b[1;33m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n",
+              "└── \u001b[1;33m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
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🚀 Crew: crew\n",
-              "└── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
+              "└── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
               "       Status: Executing Task...\n",
               "    └── 🤖 Agent: Data Quality Analyst\n",
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@@ -730,7 +746,7 @@
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-              "└── \u001b[1;32m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n",
+              "└── \u001b[1;32m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
               "    \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "    \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "    └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
@@ -738,7 +754,7 @@
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🚀 Crew: crew\n",
-              "└── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
+              "└── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
               "       Assigned to: Data Quality Analyst\n",
               "       Status: ✅ Completed\n",
               "    └── 🤖 Agent: Data Quality Analyst\n",
@@ -755,7 +771,7 @@
               "\u001b[32m╭─\u001b[0m\u001b[32m───────────────────────────────────────────────\u001b[0m\u001b[32m Task Completion \u001b[0m\u001b[32m───────────────────────────────────────────────\u001b[0m\u001b[32m─╮\u001b[0m\n",
               "\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m  \u001b[1;32mTask Completed\u001b[0m                                                                                                 \u001b[32m│\u001b[0m\n",
-              "\u001b[32m│\u001b[0m  \u001b[37mName: \u001b[0m\u001b[32m1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m                                                                     \u001b[32m│\u001b[0m\n",
+              "\u001b[32m│\u001b[0m  \u001b[37mName: \u001b[0m\u001b[32m27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m                                                                     \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m  \u001b[37mAgent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m                                                                                    \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n",
@@ -765,7 +781,7 @@
               "
╭──────────────────────────────────────────────── Task Completion ────────────────────────────────────────────────╮\n",
               "│                                                                                                                 │\n",
               "│  Task Completed                                                                                                 │\n",
-              "│  Name: 1a1b12c0-5c13-40bc-87fb-2111b26a0890                                                                     │\n",
+              "│  Name: 27b3e8e5-4f73-4516-8113-ca44b6419eb4                                                                     │\n",
               "│  Agent: Data Quality Analyst                                                                                    │\n",
               "│                                                                                                                 │\n",
               "│                                                                                                                 │\n",
@@ -793,22 +809,22 @@
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-              "├── \u001b[1;32m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
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               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "└── \u001b[1;33m📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\u001b[0m\n",
+              "└── \u001b[1;33m📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\u001b[0m\n",
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🚀 Crew: crew\n",
-              "├── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
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               "│      Assigned to: Data Quality Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Data Quality Analyst\n",
               "│           Status: ✅ Completed\n",
-              "└── 📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\n",
+              "└── 📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\n",
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\n"
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@@ -833,24 +849,24 @@
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-              "├── \u001b[1;32m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "└── \u001b[1;33m📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\u001b[0m\n",
+              "└── \u001b[1;33m📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\u001b[0m\n",
               "    \u001b[37m   Status: \u001b[0m\u001b[2;33mExecuting Task...\u001b[0m\n",
               "    └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "        \u001b[37m    Status: \u001b[0m\u001b[1;32mIn Progress\u001b[0m\n"
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             "text/html": [
               "
🚀 Crew: crew\n",
-              "├── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
+              "├── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
               "│      Assigned to: Data Quality Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Data Quality Analyst\n",
               "│           Status: ✅ Completed\n",
-              "└── 📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\n",
+              "└── 📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\n",
               "       Status: Executing Task...\n",
               "    └── 🤖 Agent: Statistical Analyst\n",
               "            Status: In Progress\n",
@@ -883,44 +899,77 @@
             "Include a brief explanation of why each identified column may be problematic. Provide any recommendations for further investigation.\n",
             "Dataset Summary:\n",
             "No Variable Stats / Values Freqs / (% of Valid) Graph Missing\n",
-            "1 PassengerId
[int64] Mean (sd) : 446.0 (257.4)
min < med < max:
1.0 < 446.0 < 891.0
IQR (CV) : 445.0 (1.7) 891 distinct values  0
(0.0%)\n",
-            "2 Survived
[int64] 1. 0
2. 1 549 (61.6%)
342 (38.4%)  0
(0.0%)\n",
-            "3 Pclass
[int64] 1. 3
2. 1
3. 2 491 (55.1%)
216 (24.2%)
184 (20.7%)  0
(0.0%)\n",
-            "4 Name
[object] 1. Dooley, Mr. Patrick
2. Braund, Mr. Owen Harris
3. Cumings, Mrs. John Bradley (Fl
4. Heikkinen, Miss. Laina
5. Futrelle, Mrs. Jacques Heath (
6. Allen, Mr. William Henry
7. Moran, Mr. James
8. McCarthy, Mr. Timothy J
9. Palsson, Master. Gosta Leonard
10. Johnson, Mrs. Oscar W (Elisabe
11. other 1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
881 (98.9%)  0
(0.0%)\n",
-            "5 Sex
[object] 1. male
2. female 577 (64.8%)
314 (35.2%)  0
(0.0%)\n",
-            "6 Age
[float64] Mean (sd) : 29.7 (14.5)
min < med < max:
0.4 < 28.0 < 80.0
IQR (CV) : 17.9 (2.0) 88 distinct values  177
(19.9%)\n",
-            "7 SibSp
[int64] 1. 0
2. 1
3. 2
4. 4
5. 3
6. 8
7. 5 608 (68.2%)
209 (23.5%)
28 (3.1%)
18 (2.0%)
16 (1.8%)
7 (0.8%)
5 (0.6%)  0
(0.0%)\n",
-            "8 Parch
[int64] 1. 0
2. 1
3. 2
4. 5
5. 3
6. 4
7. 6 678 (76.1%)
118 (13.2%)
80 (9.0%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
1 (0.1%)  0
(0.0%)\n",
-            "9 Ticket
[object] 1. 347082
2. 1601
3. CA. 2343
4. 3101295
5. CA 2144
6. 347088
7. 382652
8. S.O.C. 14879
9. 113760
10. 19950
11. other 7 (0.8%)
7 (0.8%)
7 (0.8%)
6 (0.7%)
6 (0.7%)
6 (0.7%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
4 (0.4%)
834 (93.6%)  0
(0.0%)\n",
-            "10 Fare
[float64] Mean (sd) : 32.2 (49.7)
min < med < max:
0.0 < 14.5 < 512.3
IQR (CV) : 23.1 (0.6) 248 distinct values  0
(0.0%)\n",
-            "11 Cabin
[object] 1. nan
2. G6
3. C23 C25 C27
4. B96 B98
5. F2
6. D
7. E101
8. C22 C26
9. F33
10. C83
11. other 687 (77.1%)
4 (0.4%)
4 (0.4%)
4 (0.4%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
2 (0.2%)
175 (19.6%)  687
(77.1%)\n",
-            "12 Embarked
[object] 1. S
2. C
3. Q
4. nan 644 (72.3%)
168 (18.9%)
77 (8.6%)
2 (0.2%)  2
(0.2%)\n",
+            "1 PassengerId
[int64] Mean (sd) : 446.0 (257.4)
min < med < max:
1.0 < 446.0 < 891.0
IQR (CV) : 445.0 (1.7) 891 distinct values  0
(0.0%)\n",
+            "2 Survived
[int64] 1. 0
2. 1 549 (61.6%)
342 (38.4%)  0
(0.0%)\n",
+            "3 Pclass
[int64] 1. 3
2. 1
3. 2 491 (55.1%)
216 (24.2%)
184 (20.7%)  0
(0.0%)\n",
+            "4 Name
[object] 1. Dooley, Mr. Patrick
2. Braund, Mr. Owen Harris
3. Cumings, Mrs. John Bradley (Fl
4. Heikkinen, Miss. Laina
5. Futrelle, Mrs. Jacques Heath (
6. Allen, Mr. William Henry
7. Moran, Mr. James
8. McCarthy, Mr. Timothy J
9. Palsson, Master. Gosta Leonard
10. Johnson, Mrs. Oscar W (Elisabe
11. other 1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
881 (98.9%)  0
(0.0%)\n",
+            "5 Sex
[object] 1. male
2. female 577 (64.8%)
314 (35.2%)  0
(0.0%)\n",
+            "6 Age
[float64] Mean (sd) : 29.7 (14.5)
min < med < max:
0.4 < 28.0 < 80.0
IQR (CV) : 17.9 (2.0) 88 distinct values  177
(19.9%)\n",
+            "7 SibSp
[int64] 1. 0
2. 1
3. 2
4. 4
5. 3
6. 8
7. 5 608 (68.2%)
209 (23.5%)
28 (3.1%)
18 (2.0%)
16 (1.8%)
7 (0.8%)
5 (0.6%)  0
(0.0%)\n",
+            "8 Parch
[int64] 1. 0
2. 1
3. 2
4. 5
5. 3
6. 4
7. 6 678 (76.1%)
118 (13.2%)
80 (9.0%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
1 (0.1%)  0
(0.0%)\n",
+            "9 Ticket
[object] 1. 347082
2. 1601
3. CA. 2343
4. 3101295
5. CA 2144
6. 347088
7. 382652
8. S.O.C. 14879
9. 113760
10. 19950
11. other 7 (0.8%)
7 (0.8%)
7 (0.8%)
6 (0.7%)
6 (0.7%)
6 (0.7%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
4 (0.4%)
834 (93.6%)  0
(0.0%)\n",
+            "10 Fare
[float64] Mean (sd) : 32.2 (49.7)
min < med < max:
0.0 < 14.5 < 512.3
IQR (CV) : 23.1 (0.6) 248 distinct values  0
(0.0%)\n",
+            "11 Cabin
[object] 1. nan
2. G6
3. C23 C25 C27
4. B96 B98
5. F2
6. D
7. E101
8. C22 C26
9. F33
10. C83
11. other 687 (77.1%)
4 (0.4%)
4 (0.4%)
4 (0.4%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
2 (0.2%)
175 (19.6%)  687
(77.1%)\n",
+            "12 Embarked
[object] 1. S
2. C
3. Q
4. nan 644 (72.3%)
168 (18.9%)
77 (8.6%)
2 (0.2%)  2
(0.2%)\n",
             "\u001b[00m\n",
             "\n",
             "\n",
             "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mStatistical Analyst\u001b[00m\n",
             "\u001b[95m## Final Answer:\u001b[00m \u001b[92m\n",
-            "Based on the provided dataset summary, the following numeric columns exhibit potential outlier behavior:\n",
+            "Here's a detailed analysis of the numeric columns, highlighting potential outlier issues and recommendations for further investigation:\n",
             "\n",
-            "1.  **Fare:**\n",
+            "**Numeric Columns with Potential Outliers:**\n",
             "\n",
-            "    *   **Issue:** The maximum fare (512.3) is significantly greater than three times the mean fare (32.2 \\* 3 = 96.6). This suggests the presence of passengers who paid considerably more than the average fare, potentially indicating outliers.\n",
-            "    *   **Explanation:** This large difference between the max and the mean, coupled with the standard deviation (49.7) being larger than the mean (32.2) shows a right-skewed distribution.\n",
-            "    *   **Recommendation:** Further investigate the distribution of 'Fare' using histograms and box plots to visually confirm the presence of outliers. Consider transformations (e.g., log transformation) to reduce the impact of these outliers, or explore capping or flooring 'Fare' values at certain percentiles if appropriate for the analysis goals.\n",
+            "1.  **PassengerId:**\n",
             "\n",
-            "2.  **Age:**\n",
+            "*   **Stats:** Mean: 446.0, Max: 891.0\n",
+            "*   **Analysis:** While the maximum is exactly double the mean, this is not necessarily an outlier issue for an index column. PassengerId is simply an identifier and doesn't represent a measured quantity. The values are also evenly distributed with a regular IQR.\n",
+            "*   **Recommendation:** No action needed.\n",
             "\n",
-            "    *   **Issue:** The maximum age (80.0) is more than three times the mean age(29.7 * 3 = 89.1), but not significantly, but still worth considering.\n",
-            "    *   **Explanation:** The presence of older passengers could be considered outliers depending on the context. Furthermore the 177 missing values must be accounted for before further analysis.\n",
-            "    *   **Recommendation:** Depending on the analysis context, these could be legitimate values. Impute the missing values before outlier treatment.\n",
+            "2.  **Survived:**\n",
             "\n",
-            "**Additional Considerations:**\n",
+            "*   **Stats:** Values: 0, 1\n",
+            "*   **Analysis:** This is a binary categorical variable encoded numerically. Outlier analysis is not relevant here.\n",
+            "*   **Recommendation:** No action needed.\n",
             "\n",
-            "*   **SibSp and Parch:** While not exceeding the 3x mean threshold, the presence of families with a large number of siblings/spouses or parents/children might warrant further inspection depending on the specific research question. The maximum values for these columns are relatively low, so they are less likely to be problematic outliers.\n",
-            "*   **PassengerId:** This is an index column and not relevant for outlier analysis.\n",
-            "*   **Survived and Pclass:** These are categorical variables encoded as integers and should not be treated as continuous numeric variables for outlier detection.\n",
+            "3.  **Pclass:**\n",
             "\n",
-            "These recommendations will allow for a more thorough examination of potential outliers in the dataset's numeric columns.\u001b[00m\n",
+            "*   **Stats:** Values: 1, 2, 3\n",
+            "*   **Analysis:** This is an ordinal categorical variable encoded numerically. Outlier analysis is not relevant here.\n",
+            "*   **Recommendation:** No action needed.\n",
+            "\n",
+            "4.  **Age:**\n",
+            "\n",
+            "*   **Stats:** Mean: 29.7, Max: 80.0\n",
+            "*   **Analysis:** The maximum age (80) is significantly higher than the mean (29.7), although not exceeding three times the mean. Given the context of the data (passengers on the Titanic), an age of 80 is plausible, but it's still at the upper end of the distribution. The presence of missing values should be dealt with first, as described in the previous response, as this will change the distribution and summary statistics.\n",
+            "*   **Recommendation:** Further investigation may be warranted, but after addressing the missing values, visualizations (boxplots, histograms) can clarify the distribution and the effect of imputation on the apparent outliers.\n",
+            "\n",
+            "5.  **SibSp:**\n",
+            "\n",
+            "*   **Stats:** Values range from 0 to 8.\n",
+            "*   **Analysis:**  The values range from 0 to 8 siblings/spouses aboard. The mean is not provided in the summary, however it is expected that the max of 8 will greatly exceed the mean, given the distribution.\n",
+            "*   **Recommendation:** Examine the distribution of SibSp. Values of 5 and 8 are relatively rare, so these could be considered outliers, and investigated further.\n",
+            "\n",
+            "6.  **Parch:**\n",
+            "\n",
+            "*   **Stats:** Values range from 0 to 6.\n",
+            "*   **Analysis:** The values range from 0 to 6 parents/children aboard. The mean is not provided in the summary, however it is expected that the max of 6 will greatly exceed the mean, given the distribution.\n",
+            "*   **Recommendation:** Examine the distribution of Parch. Values of 4, 5, and 6 are very rare, so these could be considered outliers, and investigated further.\n",
+            "\n",
+            "7.  **Fare:**\n",
+            "\n",
+            "*   **Stats:** Mean: 32.2, Max: 512.3\n",
+            "*   **Analysis:** The maximum fare (512.3) is significantly higher than the mean (32.2). In fact, it's more than 15 times the mean. This suggests the presence of outliers, representing passengers who paid significantly more than the average fare.\n",
+            "*   **Recommendation:** This column clearly warrants further investigation. Boxplots or histograms will help visualize the distribution and confirm the presence of outliers. Consider transformations (e.g., log transformation) to reduce the impact of outliers or capping extreme values.\n",
+            "\n",
+            "**Summary Table**\n",
+            "\n",
+            "| Column     | Potential Outliers | max > 3*mean? | Recommendation                                                                                                                                                         |\n",
+            "|------------|--------------------|----------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
+            "| Age        | Yes                | Possibly        | Investigate after imputing missing values; visualize the distribution.                                                                                                |\n",
+            "| SibSp      | Yes                | Unknown         | Visualize distribution with boxplot and histogram, and investigate values 5 and 8.                                                                                                  |\n",
+            "| Parch      | Yes                | Unknown         | Visualize distribution with boxplot and histogram, and investigate values 4, 5 and 6.                                                                                                  |\n",
+            "| Fare       | Yes                | Yes            | Visualize distribution; consider transformations or capping.                                                                                                          |\u001b[00m\n",
             "\n",
             "\n"
           ]
@@ -930,24 +979,24 @@
           "data": {
             "text/plain": [
               "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "└── \u001b[1;33m📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\u001b[0m\n",
+              "└── \u001b[1;33m📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\u001b[0m\n",
               "    \u001b[37m   Status: \u001b[0m\u001b[2;33mExecuting Task...\u001b[0m\n",
               "    └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "        \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n"
             ],
             "text/html": [
               "
🚀 Crew: crew\n",
-              "├── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
+              "├── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
               "│      Assigned to: Data Quality Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Data Quality Analyst\n",
               "│           Status: ✅ Completed\n",
-              "└── 📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\n",
+              "└── 📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\n",
               "       Status: Executing Task...\n",
               "    └── 🤖 Agent: Statistical Analyst\n",
               "            Status: ✅ Completed\n",
@@ -974,12 +1023,12 @@
           "data": {
             "text/plain": [
               "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "└── \u001b[1;32m📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\u001b[0m\n",
+              "└── \u001b[1;32m📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\u001b[0m\n",
               "    \u001b[37m   Assigned to: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "    \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "    └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
@@ -987,12 +1036,12 @@
             ],
             "text/html": [
               "
🚀 Crew: crew\n",
-              "├── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
+              "├── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
               "│      Assigned to: Data Quality Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Data Quality Analyst\n",
               "│           Status: ✅ Completed\n",
-              "└── 📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\n",
+              "└── 📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\n",
               "       Assigned to: Statistical Analyst\n",
               "       Status: ✅ Completed\n",
               "    └── 🤖 Agent: Statistical Analyst\n",
@@ -1009,7 +1058,7 @@
               "\u001b[32m╭─\u001b[0m\u001b[32m───────────────────────────────────────────────\u001b[0m\u001b[32m Task Completion \u001b[0m\u001b[32m───────────────────────────────────────────────\u001b[0m\u001b[32m─╮\u001b[0m\n",
               "\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m  \u001b[1;32mTask Completed\u001b[0m                                                                                                 \u001b[32m│\u001b[0m\n",
-              "\u001b[32m│\u001b[0m  \u001b[37mName: \u001b[0m\u001b[32m8eedb141-4888-4153-adab-2eefd60cf4ad\u001b[0m                                                                     \u001b[32m│\u001b[0m\n",
+              "\u001b[32m│\u001b[0m  \u001b[37mName: \u001b[0m\u001b[32m133ee44e-4b91-4802-8f23-e5d38d410e32\u001b[0m                                                                     \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m  \u001b[37mAgent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m                                                                                     \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n",
@@ -1019,7 +1068,7 @@
               "
╭──────────────────────────────────────────────── Task Completion ────────────────────────────────────────────────╮\n",
               "│                                                                                                                 │\n",
               "│  Task Completed                                                                                                 │\n",
-              "│  Name: 8eedb141-4888-4153-adab-2eefd60cf4ad                                                                     │\n",
+              "│  Name: 133ee44e-4b91-4802-8f23-e5d38d410e32                                                                     │\n",
               "│  Agent: Statistical Analyst                                                                                     │\n",
               "│                                                                                                                 │\n",
               "│                                                                                                                 │\n",
@@ -1047,32 +1096,32 @@
           "data": {
             "text/plain": [
               "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "└── \u001b[1;33m📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\u001b[0m\n",
+              "└── \u001b[1;33m📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\u001b[0m\n",
               "    \u001b[37m   Status: \u001b[0m\u001b[2;33mExecuting Task...\u001b[0m\n"
             ],
             "text/html": [
               "
🚀 Crew: crew\n",
-              "├── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
+              "├── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
               "│      Assigned to: Data Quality Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Data Quality Analyst\n",
               "│           Status: ✅ Completed\n",
-              "├── 📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\n",
+              "├── 📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\n",
               "│      Assigned to: Statistical Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Statistical Analyst\n",
               "│           Status: ✅ Completed\n",
-              "└── 📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\n",
+              "└── 📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\n",
               "       Status: Executing Task...\n",
               "
\n"
             ]
@@ -1097,34 +1146,34 @@
           "data": {
             "text/plain": [
               "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "└── \u001b[1;33m📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\u001b[0m\n",
+              "└── \u001b[1;33m📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\u001b[0m\n",
               "    \u001b[37m   Status: \u001b[0m\u001b[2;33mExecuting Task...\u001b[0m\n",
               "    └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mPredictive Modeling Advisor\u001b[0m\n",
               "        \u001b[37m    Status: \u001b[0m\u001b[1;32mIn Progress\u001b[0m\n"
             ],
             "text/html": [
               "
🚀 Crew: crew\n",
-              "├── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
+              "├── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
               "│      Assigned to: Data Quality Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Data Quality Analyst\n",
               "│           Status: ✅ Completed\n",
-              "├── 📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\n",
+              "├── 📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\n",
               "│      Assigned to: Statistical Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Statistical Analyst\n",
               "│           Status: ✅ Completed\n",
-              "└── 📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\n",
+              "└── 📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\n",
               "       Status: Executing Task...\n",
               "    └── 🤖 Agent: Predictive Modeling Advisor\n",
               "            Status: In Progress\n",
@@ -1155,18 +1204,18 @@
             "Based on the dataset summary below, recommend which columns might be most informative for building a predictive model.\n",
             "Dataset Summary:\n",
             "No Variable Stats / Values Freqs / (% of Valid) Graph Missing\n",
-            "1 PassengerId
[int64] Mean (sd) : 446.0 (257.4)
min < med < max:
1.0 < 446.0 < 891.0
IQR (CV) : 445.0 (1.7) 891 distinct values  0
(0.0%)\n",
-            "2 Survived
[int64] 1. 0
2. 1 549 (61.6%)
342 (38.4%)  0
(0.0%)\n",
-            "3 Pclass
[int64] 1. 3
2. 1
3. 2 491 (55.1%)
216 (24.2%)
184 (20.7%)  0
(0.0%)\n",
-            "4 Name
[object] 1. Dooley, Mr. Patrick
2. Braund, Mr. Owen Harris
3. Cumings, Mrs. John Bradley (Fl
4. Heikkinen, Miss. Laina
5. Futrelle, Mrs. Jacques Heath (
6. Allen, Mr. William Henry
7. Moran, Mr. James
8. McCarthy, Mr. Timothy J
9. Palsson, Master. Gosta Leonard
10. Johnson, Mrs. Oscar W (Elisabe
11. other 1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
881 (98.9%)  0
(0.0%)\n",
-            "5 Sex
[object] 1. male
2. female 577 (64.8%)
314 (35.2%)  0
(0.0%)\n",
-            "6 Age
[float64] Mean (sd) : 29.7 (14.5)
min < med < max:
0.4 < 28.0 < 80.0
IQR (CV) : 17.9 (2.0) 88 distinct values  177
(19.9%)\n",
-            "7 SibSp
[int64] 1. 0
2. 1
3. 2
4. 4
5. 3
6. 8
7. 5 608 (68.2%)
209 (23.5%)
28 (3.1%)
18 (2.0%)
16 (1.8%)
7 (0.8%)
5 (0.6%)  0
(0.0%)\n",
-            "8 Parch
[int64] 1. 0
2. 1
3. 2
4. 5
5. 3
6. 4
7. 6 678 (76.1%)
118 (13.2%)
80 (9.0%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
1 (0.1%)  0
(0.0%)\n",
-            "9 Ticket
[object] 1. 347082
2. 1601
3. CA. 2343
4. 3101295
5. CA 2144
6. 347088
7. 382652
8. S.O.C. 14879
9. 113760
10. 19950
11. other 7 (0.8%)
7 (0.8%)
7 (0.8%)
6 (0.7%)
6 (0.7%)
6 (0.7%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
4 (0.4%)
834 (93.6%)  0
(0.0%)\n",
-            "10 Fare
[float64] Mean (sd) : 32.2 (49.7)
min < med < max:
0.0 < 14.5 < 512.3
IQR (CV) : 23.1 (0.6) 248 distinct values  0
(0.0%)\n",
-            "11 Cabin
[object] 1. nan
2. G6
3. C23 C25 C27
4. B96 B98
5. F2
6. D
7. E101
8. C22 C26
9. F33
10. C83
11. other 687 (77.1%)
4 (0.4%)
4 (0.4%)
4 (0.4%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
2 (0.2%)
175 (19.6%)  687
(77.1%)\n",
-            "12 Embarked
[object] 1. S
2. C
3. Q
4. nan 644 (72.3%)
168 (18.9%)
77 (8.6%)
2 (0.2%)  2
(0.2%)\n",
+            "1 PassengerId
[int64] Mean (sd) : 446.0 (257.4)
min < med < max:
1.0 < 446.0 < 891.0
IQR (CV) : 445.0 (1.7) 891 distinct values  0
(0.0%)\n",
+            "2 Survived
[int64] 1. 0
2. 1 549 (61.6%)
342 (38.4%)  0
(0.0%)\n",
+            "3 Pclass
[int64] 1. 3
2. 1
3. 2 491 (55.1%)
216 (24.2%)
184 (20.7%)  0
(0.0%)\n",
+            "4 Name
[object] 1. Dooley, Mr. Patrick
2. Braund, Mr. Owen Harris
3. Cumings, Mrs. John Bradley (Fl
4. Heikkinen, Miss. Laina
5. Futrelle, Mrs. Jacques Heath (
6. Allen, Mr. William Henry
7. Moran, Mr. James
8. McCarthy, Mr. Timothy J
9. Palsson, Master. Gosta Leonard
10. Johnson, Mrs. Oscar W (Elisabe
11. other 1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
1 (0.1%)
881 (98.9%)  0
(0.0%)\n",
+            "5 Sex
[object] 1. male
2. female 577 (64.8%)
314 (35.2%)  0
(0.0%)\n",
+            "6 Age
[float64] Mean (sd) : 29.7 (14.5)
min < med < max:
0.4 < 28.0 < 80.0
IQR (CV) : 17.9 (2.0) 88 distinct values  177
(19.9%)\n",
+            "7 SibSp
[int64] 1. 0
2. 1
3. 2
4. 4
5. 3
6. 8
7. 5 608 (68.2%)
209 (23.5%)
28 (3.1%)
18 (2.0%)
16 (1.8%)
7 (0.8%)
5 (0.6%)  0
(0.0%)\n",
+            "8 Parch
[int64] 1. 0
2. 1
3. 2
4. 5
5. 3
6. 4
7. 6 678 (76.1%)
118 (13.2%)
80 (9.0%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
1 (0.1%)  0
(0.0%)\n",
+            "9 Ticket
[object] 1. 347082
2. 1601
3. CA. 2343
4. 3101295
5. CA 2144
6. 347088
7. 382652
8. S.O.C. 14879
9. 113760
10. 19950
11. other 7 (0.8%)
7 (0.8%)
7 (0.8%)
6 (0.7%)
6 (0.7%)
6 (0.7%)
5 (0.6%)
5 (0.6%)
4 (0.4%)
4 (0.4%)
834 (93.6%)  0
(0.0%)\n",
+            "10 Fare
[float64] Mean (sd) : 32.2 (49.7)
min < med < max:
0.0 < 14.5 < 512.3
IQR (CV) : 23.1 (0.6) 248 distinct values  0
(0.0%)\n",
+            "11 Cabin
[object] 1. nan
2. G6
3. C23 C25 C27
4. B96 B98
5. F2
6. D
7. E101
8. C22 C26
9. F33
10. C83
11. other 687 (77.1%)
4 (0.4%)
4 (0.4%)
4 (0.4%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
3 (0.3%)
2 (0.2%)
175 (19.6%)  687
(77.1%)\n",
+            "12 Embarked
[object] 1. S
2. C
3. Q
4. nan 644 (72.3%)
168 (18.9%)
77 (8.6%)
2 (0.2%)  2
(0.2%)\n",
             "\u001b[00m\n"
           ]
         },
@@ -1203,276 +1252,39 @@
           "data": {
             "text/plain": [
               "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "└── \u001b[1;33m📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\u001b[0m\n",
+              "└── \u001b[1;33m📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\u001b[0m\n",
               "    \u001b[37m   Status: \u001b[0m\u001b[2;33mExecuting Task...\u001b[0m\n",
               "    ├── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mPredictive Modeling Advisor\u001b[0m\n",
               "    │   \u001b[37m    Status: \u001b[0m\u001b[1;32mIn Progress\u001b[0m\n",
-              "    └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
-              "        \u001b[37m    Status: \u001b[0m\u001b[1;32mIn Progress\u001b[0m\n"
-            ],
-            "text/html": [
-              "
🚀 Crew: crew\n",
-              "├── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
-              "│      Assigned to: Data Quality Analyst\n",
-              "│      Status: ✅ Completed\n",
-              "│   └── 🤖 Agent: Data Quality Analyst\n",
-              "│           Status: ✅ Completed\n",
-              "├── 📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\n",
-              "│      Assigned to: Statistical Analyst\n",
-              "│      Status: ✅ Completed\n",
-              "│   └── 🤖 Agent: Statistical Analyst\n",
-              "│           Status: ✅ Completed\n",
-              "└── 📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\n",
-              "       Status: Executing Task...\n",
-              "    ├── 🤖 Agent: Predictive Modeling Advisor\n",
-              "    │       Status: In Progress\n",
-              "    └── 🤖 Agent: Data Quality Analyst\n",
-              "            Status: In Progress\n",
-              "
\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "\n"
-            ],
-            "text/html": [
-              "
\n",
-              "
\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mData Quality Analyst\u001b[00m\n",
-            "\u001b[95m## Task:\u001b[00m \u001b[92mBased on the data quality assessment I have, the columns 'Age', 'Cabin', and 'Embarked' have missing values. 'Age' has about 20% missing, 'Cabin' has 77% missing, and 'Embarked' has very few. I plan to impute 'Age' using mean/median/regression, create a 'Cabin_Known' binary feature for 'Cabin', and impute 'Embarked' using the mode. Do you agree with this approach? Are there any alternative strategies or potential pitfalls I should be aware of before moving forward with these columns in predictive modeling?\u001b[00m\n",
-            "\n",
-            "\n",
-            "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mData Quality Analyst\u001b[00m\n",
-            "\u001b[95m## Final Answer:\u001b[00m \u001b[92m\n",
-            "Your proposed strategies are a good starting point. Refine your approach to 'Age' by exploring different imputation techniques (including regression with caution, and considering a missingness indicator). Your approach to 'Cabin' is excellent. Your approach to 'Embarked' is appropriate given the small number of missing values. Remember to perform a thorough missing data analysis, document your steps, and evaluate your model carefully using appropriate cross-validation techniques and being mindful of data leakage to the test set. Consider algorithms that handle missing values natively.\u001b[00m\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n",
-              "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
-              "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
-              "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\u001b[0m\n",
-              "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
-              "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
-              "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "└── \u001b[1;33m📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\u001b[0m\n",
-              "    \u001b[37m   Status: \u001b[0m\u001b[2;33mExecuting Task...\u001b[0m\n",
-              "    ├── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mPredictive Modeling Advisor\u001b[0m\n",
-              "    │   \u001b[37m    Status: \u001b[0m\u001b[1;32mIn Progress\u001b[0m\n",
-              "    └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
-              "        \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n"
-            ],
-            "text/html": [
-              "
🚀 Crew: crew\n",
-              "├── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
-              "│      Assigned to: Data Quality Analyst\n",
-              "│      Status: ✅ Completed\n",
-              "│   └── 🤖 Agent: Data Quality Analyst\n",
-              "│           Status: ✅ Completed\n",
-              "├── 📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\n",
-              "│      Assigned to: Statistical Analyst\n",
-              "│      Status: ✅ Completed\n",
-              "│   └── 🤖 Agent: Statistical Analyst\n",
-              "│           Status: ✅ Completed\n",
-              "└── 📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\n",
-              "       Status: Executing Task...\n",
-              "    ├── 🤖 Agent: Predictive Modeling Advisor\n",
-              "    │       Status: In Progress\n",
-              "    └── 🤖 Agent: Data Quality Analyst\n",
-              "            Status: ✅ Completed\n",
-              "
\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "\n"
-            ],
-            "text/html": [
-              "
\n",
-              "
\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\n",
-            "\n",
-            "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mPredictive Modeling Advisor\u001b[00m\n",
-            "\u001b[95m## Thought:\u001b[00m \u001b[92mOkay, I need to recommend columns that might be most informative for building a predictive model, and I have some context about data quality and potential outliers to consider.\n",
-            "First, I'll ask the Data Quality Analyst to confirm my assessment of how to handle the missing values, as that could impact the utility of those columns. Then, I'll ask the Statistical Analyst to provide insight into which of the columns with potential outliers might be most useful for predictive modeling, even with the outliers.\u001b[00m\n",
-            "\u001b[95m## Using tool:\u001b[00m \u001b[92mAsk question to coworker\u001b[00m\n",
-            "\u001b[95m## Tool Input:\u001b[00m \u001b[92m\n",
-            "\"{\\\"question\\\": \\\"Based on the data quality assessment I have, the columns 'Age', 'Cabin', and 'Embarked' have missing values. 'Age' has about 20% missing, 'Cabin' has 77% missing, and 'Embarked' has very few. I plan to impute 'Age' using mean/median/regression, create a 'Cabin_Known' binary feature for 'Cabin', and impute 'Embarked' using the mode. Do you agree with this approach? Are there any alternative strategies or potential pitfalls I should be aware of before moving forward with these columns in predictive modeling?\\\", \\\"context\\\": \\\"Dataset contains missing values in columns 'Age', 'Cabin' and 'Embarked'. Need to know if the imputation strategy is appropriate and if not, what would be the correct one\\\", \\\"coworker\\\": \\\"Data Quality Analyst\\\"}\"\u001b[00m\n",
-            "\u001b[95m## Tool Output:\u001b[00m \u001b[92m\n",
-            "Your proposed strategies are a good starting point. Refine your approach to 'Age' by exploring different imputation techniques (including regression with caution, and considering a missingness indicator). Your approach to 'Cabin' is excellent. Your approach to 'Embarked' is appropriate given the small number of missing values. Remember to perform a thorough missing data analysis, document your steps, and evaluate your model carefully using appropriate cross-validation techniques and being mindful of data leakage to the test set. Consider algorithms that handle missing values natively.\u001b[00m\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "\u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
-              "\u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "└── \u001b[1;34m🧠 \u001b[0m\u001b[34mThinking...\u001b[0m\n"
-            ],
-            "text/html": [
-              "
🤖 Agent: Data Quality Analyst\n",
-              "    Status: ✅ Completed\n",
-              "└── 🧠 Thinking...\n",
-              "
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-            ]
-          },
-          "metadata": {}
-        },
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-              "\u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
-              "\u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n"
-            ],
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-              "
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-              "\u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
-              "\u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n"
-            ],
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🤖 Agent: Data Quality Analyst\n",
-              "    Status: ✅ Completed\n",
-              "
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-            ]
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-              "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n",
-              "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
-              "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
-              "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\u001b[0m\n",
-              "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
-              "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
-              "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "└── \u001b[1;33m📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\u001b[0m\n",
-              "    \u001b[37m   Status: \u001b[0m\u001b[2;33mExecuting Task...\u001b[0m\n",
-              "    ├── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mPredictive Modeling Advisor\u001b[0m\n",
-              "    │   \u001b[37m    Status: \u001b[0m\u001b[1;32mIn Progress\u001b[0m\n",
-              "    ├── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
-              "    │   \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "    └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "        \u001b[37m    Status: \u001b[0m\u001b[1;32mIn Progress\u001b[0m\n"
             ],
             "text/html": [
               "
🚀 Crew: crew\n",
-              "├── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
+              "├── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
               "│      Assigned to: Data Quality Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Data Quality Analyst\n",
               "│           Status: ✅ Completed\n",
-              "├── 📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\n",
+              "├── 📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\n",
               "│      Assigned to: Statistical Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Statistical Analyst\n",
               "│           Status: ✅ Completed\n",
-              "└── 📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\n",
+              "└── 📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\n",
               "       Status: Executing Task...\n",
               "    ├── 🤖 Agent: Predictive Modeling Advisor\n",
               "    │       Status: In Progress\n",
-              "    ├── 🤖 Agent: Data Quality Analyst\n",
-              "    │       Status: ✅ Completed\n",
               "    └── 🤖 Agent: Statistical Analyst\n",
               "            Status: In Progress\n",
               "
\n"
@@ -1498,47 +1310,12 @@
           "name": "stdout",
           "text": [
             "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mStatistical Analyst\u001b[00m\n",
-            "\u001b[95m## Task:\u001b[00m \u001b[92mBased on the dataset summary, 'Fare' and 'Age' exhibit potential outlier behavior, where the maximum values are significantly larger than the mean. I plan to investigate the distributions and potentially apply transformations (e.g., log transform) or capping/flooring. Do you think these columns, even with potential outliers, are likely to be informative for predicting 'Survived'? Are there any other columns that I should examine for potential non-linear relationships or interactions with other features that might be relevant for predictive modeling?\u001b[00m\n",
+            "\u001b[95m## Task:\u001b[00m \u001b[92mBased on the dataset summary provided and considering potential outliers in 'SibSp' and 'Parch' columns, do you recommend combining these features into a 'FamilySize' feature? Also, considering the high cardinality of 'Name' and 'Ticket' columns, do you think it's worth investing time in extracting titles from 'Name' or prefixes from 'Ticket' columns, or are these likely to introduce more noise than signal?\u001b[00m\n",
             "\n",
             "\n",
             "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mStatistical Analyst\u001b[00m\n",
             "\u001b[95m## Final Answer:\u001b[00m \u001b[92m\n",
-            "Yes, even with potential outliers, both 'Fare' and 'Age' are likely to be informative for predicting 'Survived.' Here's why:\n",
-            "\n",
-            "*   **Fare:** Ticket fare is often correlated with socio-economic status, which historically has a strong relationship with survival rates on the Titanic. Higher fares typically meant a higher class cabin located on upper decks, which had better access to lifeboats. Even with extreme values, the 'Fare' column likely captures important information about a passenger's social standing and priority during the evacuation. The outliers themselves might be informative; extremely high fares could represent first-class passengers who received preferential treatment.\n",
-            "\n",
-            "*   **Age:** Age can also be a significant factor. Survival rates often differ between age groups, with children and the elderly sometimes receiving priority (though this might vary based on the specific event). Outliers in 'Age' (very old passengers) could represent a vulnerable group. Conversely, very young passengers may have been prioritized. Furthermore, even if age doesn't have a perfectly linear relationship with survival, it can still be a useful predictor when modeled appropriately (e.g., using splines or binning).\n",
-            "\n",
-            "Recommendations for handling 'Fare' and 'Age':\n",
-            "\n",
-            "1.  **Explore the Distributions:** Visualize the distributions using histograms, box plots, and quantile plots to understand the extent and nature of the outliers. Look for skewness and potential multi-modality (multiple peaks).\n",
-            "\n",
-            "2.  **Outlier Treatment:** Consider the following approaches:\n",
-            "\n",
-            "    *   **Transformation:** A log transformation can help reduce the impact of extreme values and make the distribution more symmetrical. However, be cautious about applying log transformation to zero values (you may need to add a small constant).\n",
-            "    *   **Winsorizing/Capping:** Limit extreme values to a certain percentile (e.g., 95th or 99th). This reduces the influence of outliers without completely removing them.\n",
-            "    *   **Binning:** Group ages into categories (e.g., children, young adults, adults, seniors). This can capture non-linear relationships.\n",
-            "    *   **No Treatment:** Depending on the modeling algorithm (tree-based models are relatively robust to outliers) and the extent of the outliers' influence on model performance, you might choose to leave them as is.\n",
-            "\n",
-            "3.  **Interaction Effects:** Explore potential interactions between 'Fare' and 'Age' with other features:\n",
-            "\n",
-            "    *   **Fare and Pclass:** The combination of fare and passenger class ('Pclass') can provide a more nuanced view of socio-economic status. For example, a high fare in a lower class might indicate a special circumstance or a larger group.\n",
-            "    *   **Age and Sex:** Survival patterns might differ significantly between male and female passengers within specific age groups. This interaction is almost certainly important.\n",
-            "    *   **Age and SibSp/Parch:** The number of siblings/spouses ('SibSp') and parents/children ('Parch') traveling with a passenger might influence survival chances, especially for children and the elderly. Traveling with family might mean higher priority for assistance or, conversely, more difficulty in evacuating.\n",
-            "\n",
-            "Other Columns to Examine for Non-Linear Relationships and Interactions:\n",
-            "\n",
-            "*   **Pclass:** Passenger class is inherently categorical, but its relationship with 'Survived' may not be linear. Consider creating dummy variables or using an ordinal encoding (if you believe there's a natural ordering). Interact 'Pclass' with 'Sex' and 'Age'.\n",
-            "\n",
-            "*   **SibSp and Parch:** These represent family size. Instead of treating them as continuous variables, consider creating a 'FamilySize' feature (SibSp + Parch + 1). You could also create a binary feature indicating whether a passenger was traveling alone. The relationship between family size and survival may not be linear (e.g., larger families might face more challenges).\n",
-            "\n",
-            "*   **Embarked:** The port of embarkation might be correlated with survival due to class distribution or lifeboat availability. Create dummy variables for this feature. Interactions with 'Pclass' are likely here.\n",
-            "\n",
-            "*   **Name:** While seemingly irrelevant, the 'Name' column can be used to extract titles (e.g., \"Mr.,\" \"Mrs.,\" \"Dr.,\" \"Master\"). These titles can be indicative of social status or age (e.g., \"Master\" typically refers to young boys). Create a new feature 'Title' and explore its relationship with 'Survived,' potentially interacting it with 'Sex' and 'Age'.\n",
-            "\n",
-            "*   **Cabin:** If the 'Cabin' column has sufficient data, the cabin location could be correlated with survival rates. Extract the cabin deck (e.g., 'A', 'B', 'C') and treat it as a categorical variable. Many values are missing, so handle with care (missing could be a meaningful category).\n",
-            "\n",
-            "In summary, 'Fare' and 'Age' are likely valuable predictors despite the presence of outliers. Carefully explore their distributions, consider appropriate transformations or capping methods, and investigate potential interaction effects with other features, particularly 'Pclass,' 'Sex,' and family size variables. Also, scrutinize other categorical or quasi-categorical columns like 'Pclass', 'Embarked', 'SibSp', 'Parch' and derive features from 'Name' and 'Cabin' to potentially improve predictive power. Always validate your choices using appropriate evaluation metrics and cross-validation to avoid overfitting.\u001b[00m\n",
+            "Combining 'SibSp' and 'Parch' into 'FamilySize' is recommended to address outliers and create a more informative feature. Extracting titles from the 'Name' column should be explored as a potentially valuable signal reflecting social status, age, and gender. However, I advise against investing significant time in extracting prefixes from the 'Ticket' column due to the high risk of introducing noise without a clear, meaningful relationship to survival. Prioritize feature engineering efforts on 'Name' given the limited time.\u001b[00m\n",
             "\n",
             "\n"
           ]
@@ -1548,43 +1325,39 @@
           "data": {
             "text/plain": [
               "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "└── \u001b[1;33m📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\u001b[0m\n",
+              "└── \u001b[1;33m📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\u001b[0m\n",
               "    \u001b[37m   Status: \u001b[0m\u001b[2;33mExecuting Task...\u001b[0m\n",
               "    ├── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mPredictive Modeling Advisor\u001b[0m\n",
               "    │   \u001b[37m    Status: \u001b[0m\u001b[1;32mIn Progress\u001b[0m\n",
-              "    ├── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
-              "    │   \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "    └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "        \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n"
             ],
             "text/html": [
               "
🚀 Crew: crew\n",
-              "├── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
+              "├── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
               "│      Assigned to: Data Quality Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Data Quality Analyst\n",
               "│           Status: ✅ Completed\n",
-              "├── 📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\n",
+              "├── 📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\n",
               "│      Assigned to: Statistical Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Statistical Analyst\n",
               "│           Status: ✅ Completed\n",
-              "└── 📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\n",
+              "└── 📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\n",
               "       Status: Executing Task...\n",
               "    ├── 🤖 Agent: Predictive Modeling Advisor\n",
               "    │       Status: In Progress\n",
-              "    ├── 🤖 Agent: Data Quality Analyst\n",
-              "    │       Status: ✅ Completed\n",
               "    └── 🤖 Agent: Statistical Analyst\n",
               "            Status: ✅ Completed\n",
               "
\n"
@@ -1612,47 +1385,20 @@
             "\n",
             "\n",
             "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mPredictive Modeling Advisor\u001b[00m\n",
-            "\u001b[95m## Thought:\u001b[00m \u001b[92mThought: Now that the Data Quality Analyst has confirmed my assessment, I can ask the Statistical Analyst to help determine the most useful columns for the predictive model, given the potential outlier issues.\u001b[00m\n",
+            "\u001b[95m## Thought:\u001b[00m \u001b[92mOkay, based on the dataset summary and the additional analyses, I need to identify the columns that are most likely to be informative for predictive modeling. Here's my plan:\n",
+            "1.  **Target Variable:** 'Survived' is the target variable, so it's obviously important.\n",
+            "2.  **Categorical Features:** 'Pclass', 'Sex', and 'Embarked' seem potentially informative, representing passenger class, gender, and port of embarkation respectively.\n",
+            "3.  **Continuous Features:** 'Age' and 'Fare' could also be strong predictors, but I need to consider the missing values and potential outliers.\n",
+            "4.  **Family Size Features:** 'SibSp' and 'Parch' represent the number of siblings/spouses and parents/children aboard, respectively. These could be informative individually or combined into a \"FamilySize\" feature.\n",
+            "5.  **Name and Ticket:** 'Name' and 'Ticket' are less obvious. 'Name' might contain titles that could be indicative of social status or age. 'Ticket' is a mix of alphanumeric values that are hard to interpret.\n",
+            "6.  **Cabin:** 'Cabin' has too many missing values to be directly useful, but feature engineering (extracting the deck letter) might salvage some information.\n",
+            "7.  **PassengerId:** 'PassengerId' is just an identifier and should not be used in the model.\n",
+            "Before making a final decision, I want to get a statistical analyst's opinion on whether combining `SibSp` and `Parch` is recommended, given the potential outlier values. Also, I want to get their opinion on `Name` and `Ticket`, especially if the effort to extract titles from names and/or the ticket prefixes is worth it.\u001b[00m\n",
             "\u001b[95m## Using tool:\u001b[00m \u001b[92mAsk question to coworker\u001b[00m\n",
             "\u001b[95m## Tool Input:\u001b[00m \u001b[92m\n",
-            "\"{\\\"question\\\": \\\"Based on the dataset summary, 'Fare' and 'Age' exhibit potential outlier behavior, where the maximum values are significantly larger than the mean. I plan to investigate the distributions and potentially apply transformations (e.g., log transform) or capping/flooring. Do you think these columns, even with potential outliers, are likely to be informative for predicting 'Survived'? Are there any other columns that I should examine for potential non-linear relationships or interactions with other features that might be relevant for predictive modeling?\\\", \\\"context\\\": \\\"Dataset has potential outlier issues in 'Fare' and 'Age' columns. Need to know if they are useful for prediction and if there are any other interaction effects to consider.\\\", \\\"coworker\\\": \\\"Statistical Analyst\\\"}\"\u001b[00m\n",
+            "\"{\\\"question\\\": \\\"Based on the dataset summary provided and considering potential outliers in 'SibSp' and 'Parch' columns, do you recommend combining these features into a 'FamilySize' feature? Also, considering the high cardinality of 'Name' and 'Ticket' columns, do you think it's worth investing time in extracting titles from 'Name' or prefixes from 'Ticket' columns, or are these likely to introduce more noise than signal?\\\", \\\"context\\\": \\\"The goal is to identify the most informative columns for predicting passenger survival on the Titanic. The dataset summary contains information on each column, including data type, value frequencies, and missing values. 'SibSp' and 'Parch' have potential outlier values. 'Name' and 'Ticket' have high cardinality.\\\", \\\"coworker\\\": \\\"Statistical Analyst\\\"}\"\u001b[00m\n",
             "\u001b[95m## Tool Output:\u001b[00m \u001b[92m\n",
-            "Yes, even with potential outliers, both 'Fare' and 'Age' are likely to be informative for predicting 'Survived.' Here's why:\n",
-            "\n",
-            "*   **Fare:** Ticket fare is often correlated with socio-economic status, which historically has a strong relationship with survival rates on the Titanic. Higher fares typically meant a higher class cabin located on upper decks, which had better access to lifeboats. Even with extreme values, the 'Fare' column likely captures important information about a passenger's social standing and priority during the evacuation. The outliers themselves might be informative; extremely high fares could represent first-class passengers who received preferential treatment.\n",
-            "\n",
-            "*   **Age:** Age can also be a significant factor. Survival rates often differ between age groups, with children and the elderly sometimes receiving priority (though this might vary based on the specific event). Outliers in 'Age' (very old passengers) could represent a vulnerable group. Conversely, very young passengers may have been prioritized. Furthermore, even if age doesn't have a perfectly linear relationship with survival, it can still be a useful predictor when modeled appropriately (e.g., using splines or binning).\n",
-            "\n",
-            "Recommendations for handling 'Fare' and 'Age':\n",
-            "\n",
-            "1.  **Explore the Distributions:** Visualize the distributions using histograms, box plots, and quantile plots to understand the extent and nature of the outliers. Look for skewness and potential multi-modality (multiple peaks).\n",
-            "\n",
-            "2.  **Outlier Treatment:** Consider the following approaches:\n",
-            "\n",
-            "    *   **Transformation:** A log transformation can help reduce the impact of extreme values and make the distribution more symmetrical. However, be cautious about applying log transformation to zero values (you may need to add a small constant).\n",
-            "    *   **Winsorizing/Capping:** Limit extreme values to a certain percentile (e.g., 95th or 99th). This reduces the influence of outliers without completely removing them.\n",
-            "    *   **Binning:** Group ages into categories (e.g., children, young adults, adults, seniors). This can capture non-linear relationships.\n",
-            "    *   **No Treatment:** Depending on the modeling algorithm (tree-based models are relatively robust to outliers) and the extent of the outliers' influence on model performance, you might choose to leave them as is.\n",
-            "\n",
-            "3.  **Interaction Effects:** Explore potential interactions between 'Fare' and 'Age' with other features:\n",
-            "\n",
-            "    *   **Fare and Pclass:** The combination of fare and passenger class ('Pclass') can provide a more nuanced view of socio-economic status. For example, a high fare in a lower class might indicate a special circumstance or a larger group.\n",
-            "    *   **Age and Sex:** Survival patterns might differ significantly between male and female passengers within specific age groups. This interaction is almost certainly important.\n",
-            "    *   **Age and SibSp/Parch:** The number of siblings/spouses ('SibSp') and parents/children ('Parch') traveling with a passenger might influence survival chances, especially for children and the elderly. Traveling with family might mean higher priority for assistance or, conversely, more difficulty in evacuating.\n",
-            "\n",
-            "Other Columns to Examine for Non-Linear Relationships and Interactions:\n",
-            "\n",
-            "*   **Pclass:** Passenger class is inherently categorical, but its relationship with 'Survived' may not be linear. Consider creating dummy variables or using an ordinal encoding (if you believe there's a natural ordering). Interact 'Pclass' with 'Sex' and 'Age'.\n",
-            "\n",
-            "*   **SibSp and Parch:** These represent family size. Instead of treating them as continuous variables, consider creating a 'FamilySize' feature (SibSp + Parch + 1). You could also create a binary feature indicating whether a passenger was traveling alone. The relationship between family size and survival may not be linear (e.g., larger families might face more challenges).\n",
-            "\n",
-            "*   **Embarked:** The port of embarkation might be correlated with survival due to class distribution or lifeboat availability. Create dummy variables for this feature. Interactions with 'Pclass' are likely here.\n",
-            "\n",
-            "*   **Name:** While seemingly irrelevant, the 'Name' column can be used to extract titles (e.g., \"Mr.,\" \"Mrs.,\" \"Dr.,\" \"Master\"). These titles can be indicative of social status or age (e.g., \"Master\" typically refers to young boys). Create a new feature 'Title' and explore its relationship with 'Survived,' potentially interacting it with 'Sex' and 'Age'.\n",
-            "\n",
-            "*   **Cabin:** If the 'Cabin' column has sufficient data, the cabin location could be correlated with survival rates. Extract the cabin deck (e.g., 'A', 'B', 'C') and treat it as a categorical variable. Many values are missing, so handle with care (missing could be a meaningful category).\n",
-            "\n",
-            "In summary, 'Fare' and 'Age' are likely valuable predictors despite the presence of outliers. Carefully explore their distributions, consider appropriate transformations or capping methods, and investigate potential interaction effects with other features, particularly 'Pclass,' 'Sex,' and family size variables. Also, scrutinize other categorical or quasi-categorical columns like 'Pclass', 'Embarked', 'SibSp', 'Parch' and derive features from 'Name' and 'Cabin' to potentially improve predictive power. Always validate your choices using appropriate evaluation metrics and cross-validation to avoid overfitting.\u001b[00m\n"
+            "Combining 'SibSp' and 'Parch' into 'FamilySize' is recommended to address outliers and create a more informative feature. Extracting titles from the 'Name' column should be explored as a potentially valuable signal reflecting social status, age, and gender. However, I advise against investing significant time in extracting prefixes from the 'Ticket' column due to the high risk of introducing noise without a clear, meaningful relationship to survival. Prioritize feature engineering efforts on 'Name' given the limited time.\u001b[00m\n"
           ]
         },
         {
@@ -1713,15 +1459,443 @@
           },
           "metadata": {}
         },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
+              "\u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n"
+            ],
+            "text/html": [
+              "
🤖 Agent: Statistical Analyst\n",
+              "    Status: ✅ Completed\n",
+              "
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+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
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+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
+              "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
+              "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
+              "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
+              "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\u001b[0m\n",
+              "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
+              "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
+              "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
+              "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
+              "└── \u001b[1;33m📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\u001b[0m\n",
+              "    \u001b[37m   Status: \u001b[0m\u001b[2;33mExecuting Task...\u001b[0m\n",
+              "    ├── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mPredictive Modeling Advisor\u001b[0m\n",
+              "    │   \u001b[37m    Status: \u001b[0m\u001b[1;32mIn Progress\u001b[0m\n",
+              "    └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
+              "        \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n"
+            ],
+            "text/html": [
+              "
🚀 Crew: crew\n",
+              "├── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
+              "│      Assigned to: Data Quality Analyst\n",
+              "│      Status: ✅ Completed\n",
+              "│   └── 🤖 Agent: Data Quality Analyst\n",
+              "│           Status: ✅ Completed\n",
+              "├── 📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\n",
+              "│      Assigned to: Statistical Analyst\n",
+              "│      Status: ✅ Completed\n",
+              "│   └── 🤖 Agent: Statistical Analyst\n",
+              "│           Status: ✅ Completed\n",
+              "└── 📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\n",
+              "       Status: Executing Task...\n",
+              "    ├── 🤖 Agent: Predictive Modeling Advisor\n",
+              "    │       Status: In Progress\n",
+              "    └── 🤖 Agent: Statistical Analyst\n",
+              "            Status: ✅ Completed\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[31m╭─\u001b[0m\u001b[31m─────────────────────────────────────────────────\u001b[0m\u001b[31m Tool Error \u001b[0m\u001b[31m──────────────────────────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
+              "\u001b[31m│\u001b[0m                                                                                                                 \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[1;31mTool Usage Failed\u001b[0m                                                                                              \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[37mName: \u001b[0m\u001b[31mDelegate work to coworker\u001b[0m                                                                                \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[37mError: \u001b[0m\u001b[31mArguments validation failed: 2 validation errors for DelegateWorkToolSchema\u001b[0m                             \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31mtask\u001b[0m                                                                                                           \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m  Input should be a valid string [type=string_type, input_value={'description': \"Perform ...ySize'.\", 'type':\u001b[0m  \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m'str'}, input_type=dict]\u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m    For further information visit https://errors.pydantic.dev/2.11/v/string_type\u001b[0m                               \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31mcontext\u001b[0m                                                                                                        \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m  Input should be a valid string [type=string_type, input_value={'description': \"The goal...s made.\", 'type':\u001b[0m  \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m'str'}, input_type=dict]\u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m    For further information visit https://errors.pydantic.dev/2.11/v/string_type\u001b[0m                               \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m                                                                                                                 \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m                                                                                                                 \u001b[31m│\u001b[0m\n",
+              "\u001b[31m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+            ],
+            "text/html": [
+              "
╭────────────────────────────────────────────────── Tool Error ───────────────────────────────────────────────────╮\n",
+              "│                                                                                                                 │\n",
+              "│  Tool Usage Failed                                                                                              │\n",
+              "│  Name: Delegate work to coworker                                                                                │\n",
+              "│  Error: Arguments validation failed: 2 validation errors for DelegateWorkToolSchema                             │\n",
+              "│  task                                                                                                           │\n",
+              "│    Input should be a valid string [type=string_type, input_value={'description': \"Perform ...ySize'.\", 'type':  │\n",
+              "│  'str'}, input_type=dict]                                                                                       │\n",
+              "│      For further information visit https://errors.pydantic.dev/2.11/v/string_type                               │\n",
+              "│  context                                                                                                        │\n",
+              "│    Input should be a valid string [type=string_type, input_value={'description': \"The goal...s made.\", 'type':  │\n",
+              "│  'str'}, input_type=dict]                                                                                       │\n",
+              "│      For further information visit https://errors.pydantic.dev/2.11/v/string_type                               │\n",
+              "│                                                                                                                 │\n",
+              "│                                                                                                                 │\n",
+              "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
+              "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
+              "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
+              "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
+              "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\u001b[0m\n",
+              "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
+              "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
+              "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
+              "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
+              "└── \u001b[1;33m📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\u001b[0m\n",
+              "    \u001b[37m   Status: \u001b[0m\u001b[2;33mExecuting Task...\u001b[0m\n",
+              "    ├── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mPredictive Modeling Advisor\u001b[0m\n",
+              "    │   \u001b[37m    Status: \u001b[0m\u001b[1;32mIn Progress\u001b[0m\n",
+              "    └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
+              "        \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n"
+            ],
+            "text/html": [
+              "
🚀 Crew: crew\n",
+              "├── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
+              "│      Assigned to: Data Quality Analyst\n",
+              "│      Status: ✅ Completed\n",
+              "│   └── 🤖 Agent: Data Quality Analyst\n",
+              "│           Status: ✅ Completed\n",
+              "├── 📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\n",
+              "│      Assigned to: Statistical Analyst\n",
+              "│      Status: ✅ Completed\n",
+              "│   └── 🤖 Agent: Statistical Analyst\n",
+              "│           Status: ✅ Completed\n",
+              "└── 📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\n",
+              "       Status: Executing Task...\n",
+              "    ├── 🤖 Agent: Predictive Modeling Advisor\n",
+              "    │       Status: In Progress\n",
+              "    └── 🤖 Agent: Statistical Analyst\n",
+              "            Status: ✅ Completed\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[31m╭─\u001b[0m\u001b[31m─────────────────────────────────────────────────\u001b[0m\u001b[31m Tool Error \u001b[0m\u001b[31m──────────────────────────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
+              "\u001b[31m│\u001b[0m                                                                                                                 \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[1;31mTool Usage Failed\u001b[0m                                                                                              \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[37mName: \u001b[0m\u001b[31mDelegate work to coworker\u001b[0m                                                                                \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[37mError: \u001b[0m\u001b[31mArguments validation failed: 2 validation errors for DelegateWorkToolSchema\u001b[0m                             \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31mtask\u001b[0m                                                                                                           \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m  Input should be a valid string [type=string_type, input_value={'description': \"Perform ...ySize'.\", 'type':\u001b[0m  \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m'str'}, input_type=dict]\u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m    For further information visit https://errors.pydantic.dev/2.11/v/string_type\u001b[0m                               \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31mcontext\u001b[0m                                                                                                        \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m  Input should be a valid string [type=string_type, input_value={'description': \"The goal...s made.\", 'type':\u001b[0m  \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m'str'}, input_type=dict]\u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m    For further information visit https://errors.pydantic.dev/2.11/v/string_type\u001b[0m                               \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m                                                                                                                 \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m                                                                                                                 \u001b[31m│\u001b[0m\n",
+              "\u001b[31m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+            ],
+            "text/html": [
+              "
╭────────────────────────────────────────────────── Tool Error ───────────────────────────────────────────────────╮\n",
+              "│                                                                                                                 │\n",
+              "│  Tool Usage Failed                                                                                              │\n",
+              "│  Name: Delegate work to coworker                                                                                │\n",
+              "│  Error: Arguments validation failed: 2 validation errors for DelegateWorkToolSchema                             │\n",
+              "│  task                                                                                                           │\n",
+              "│    Input should be a valid string [type=string_type, input_value={'description': \"Perform ...ySize'.\", 'type':  │\n",
+              "│  'str'}, input_type=dict]                                                                                       │\n",
+              "│      For further information visit https://errors.pydantic.dev/2.11/v/string_type                               │\n",
+              "│  context                                                                                                        │\n",
+              "│    Input should be a valid string [type=string_type, input_value={'description': \"The goal...s made.\", 'type':  │\n",
+              "│  'str'}, input_type=dict]                                                                                       │\n",
+              "│      For further information visit https://errors.pydantic.dev/2.11/v/string_type                               │\n",
+              "│                                                                                                                 │\n",
+              "│                                                                                                                 │\n",
+              "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
+              "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
+              "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
+              "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
+              "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\u001b[0m\n",
+              "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
+              "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
+              "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
+              "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
+              "└── \u001b[1;33m📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\u001b[0m\n",
+              "    \u001b[37m   Status: \u001b[0m\u001b[2;33mExecuting Task...\u001b[0m\n",
+              "    ├── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mPredictive Modeling Advisor\u001b[0m\n",
+              "    │   \u001b[37m    Status: \u001b[0m\u001b[1;32mIn Progress\u001b[0m\n",
+              "    └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
+              "        \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n"
+            ],
+            "text/html": [
+              "
🚀 Crew: crew\n",
+              "├── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
+              "│      Assigned to: Data Quality Analyst\n",
+              "│      Status: ✅ Completed\n",
+              "│   └── 🤖 Agent: Data Quality Analyst\n",
+              "│           Status: ✅ Completed\n",
+              "├── 📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\n",
+              "│      Assigned to: Statistical Analyst\n",
+              "│      Status: ✅ Completed\n",
+              "│   └── 🤖 Agent: Statistical Analyst\n",
+              "│           Status: ✅ Completed\n",
+              "└── 📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\n",
+              "       Status: Executing Task...\n",
+              "    ├── 🤖 Agent: Predictive Modeling Advisor\n",
+              "    │       Status: In Progress\n",
+              "    └── 🤖 Agent: Statistical Analyst\n",
+              "            Status: ✅ Completed\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[31m╭─\u001b[0m\u001b[31m─────────────────────────────────────────────────\u001b[0m\u001b[31m Tool Error \u001b[0m\u001b[31m──────────────────────────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
+              "\u001b[31m│\u001b[0m                                                                                                                 \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[1;31mTool Usage Failed\u001b[0m                                                                                              \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[37mName: \u001b[0m\u001b[31mDelegate work to coworker\u001b[0m                                                                                \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[37mError: \u001b[0m\u001b[31mArguments validation failed: 2 validation errors for DelegateWorkToolSchema\u001b[0m                             \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31mtask\u001b[0m                                                                                                           \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m  Input should be a valid string [type=string_type, input_value={'description': \"Perform ...ySize'.\", 'type':\u001b[0m  \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m'str'}, input_type=dict]\u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m    For further information visit https://errors.pydantic.dev/2.11/v/string_type\u001b[0m                               \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31mcontext\u001b[0m                                                                                                        \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m  Input should be a valid string [type=string_type, input_value={'description': \"The goal...s made.\", 'type':\u001b[0m  \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m'str'}, input_type=dict]\u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m  \u001b[31m    For further information visit https://errors.pydantic.dev/2.11/v/string_type\u001b[0m                               \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m                                                                                                                 \u001b[31m│\u001b[0m\n",
+              "\u001b[31m│\u001b[0m                                                                                                                 \u001b[31m│\u001b[0m\n",
+              "\u001b[31m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+            ],
+            "text/html": [
+              "
╭────────────────────────────────────────────────── Tool Error ───────────────────────────────────────────────────╮\n",
+              "│                                                                                                                 │\n",
+              "│  Tool Usage Failed                                                                                              │\n",
+              "│  Name: Delegate work to coworker                                                                                │\n",
+              "│  Error: Arguments validation failed: 2 validation errors for DelegateWorkToolSchema                             │\n",
+              "│  task                                                                                                           │\n",
+              "│    Input should be a valid string [type=string_type, input_value={'description': \"Perform ...ySize'.\", 'type':  │\n",
+              "│  'str'}, input_type=dict]                                                                                       │\n",
+              "│      For further information visit https://errors.pydantic.dev/2.11/v/string_type                               │\n",
+              "│  context                                                                                                        │\n",
+              "│    Input should be a valid string [type=string_type, input_value={'description': \"The goal...s made.\", 'type':  │\n",
+              "│  'str'}, input_type=dict]                                                                                       │\n",
+              "│      For further information visit https://errors.pydantic.dev/2.11/v/string_type                               │\n",
+              "│                                                                                                                 │\n",
+              "│                                                                                                                 │\n",
+              "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
+            "\u001b[91m \n",
+            "\n",
+            "I encountered an error while trying to use the tool. This was the error: Arguments validation failed: 2 validation errors for DelegateWorkToolSchema\n",
+            "task\n",
+            "  Input should be a valid string [type=string_type, input_value={'description': \"Perform ...ySize'.\", 'type': 'str'}, input_type=dict]\n",
+            "    For further information visit https://errors.pydantic.dev/2.11/v/string_type\n",
+            "context\n",
+            "  Input should be a valid string [type=string_type, input_value={'description': \"The goal...s made.\", 'type': 'str'}, input_type=dict]\n",
+            "    For further information visit https://errors.pydantic.dev/2.11/v/string_type.\n",
+            " Tool Delegate work to coworker accepts these inputs: Tool Name: Delegate work to coworker\n",
+            "Tool Arguments: {'task': {'description': 'The task to delegate', 'type': 'str'}, 'context': {'description': 'The context for the task', 'type': 'str'}, 'coworker': {'description': 'The role/name of the coworker to delegate to', 'type': 'str'}}\n",
+            "Tool Description: Delegate a specific task to one of the following coworkers: Data Quality Analyst, Statistical Analyst\n",
+            "The input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolutely everything you know, don't reference things but instead explain them.\n",
+            "\u001b[00m\n",
+            "\n",
+            "\n",
+            "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mPredictive Modeling Advisor\u001b[00m\n",
+            "\u001b[95m## Thought:\u001b[00m \u001b[92mThought: I now have enough information to make a recommendation.\n",
+            "I will include the following columns:\n",
+            "*   **Survived:** (Target variable)\n",
+            "*   **Pclass:** (Categorical feature)\n",
+            "*   **Sex:** (Categorical feature)\n",
+            "*   **Age:** (Continuous feature, needs imputation)\n",
+            "*   **Fare:** (Continuous feature, potential outliers)\n",
+            "*   **Embarked:** (Categorical feature, minimal missing values)\n",
+            "*   **FamilySize:** (Combined 'SibSp' and 'Parch')\n",
+            "*   **Title:** (Extracted from 'Name')\u001b[00m\n",
+            "\u001b[95m## Using tool:\u001b[00m \u001b[92mDelegate work to coworker\u001b[00m\n",
+            "\u001b[95m## Tool Input:\u001b[00m \u001b[92m\n",
+            "\"{\\\"task\\\": {\\\"description\\\": \\\"Perform exploratory data analysis (EDA) focusing on the 'Age', 'Fare', and 'FamilySize' features. Specifically: 1. Impute missing values in 'Age' using an appropriate method (e.g., mean, median, or regression imputation). 2. Analyze the distribution of 'Fare' and address potential outliers (e.g., using transformations or capping). 3. Combine 'SibSp' and 'Parch' into 'FamilySize'.\\\", \\\"type\\\": \\\"str\\\"}, \\\"context\\\": {\\\"description\\\": \\\"The goal is to prepare the data for predictive modeling of Titanic passenger survival. 'Age' has missing values, 'Fare' has potential outliers, and 'SibSp' and 'Parch' can be combined into a more useful feature. The task should include data exploration to justify any decisions made.\\\", \\\"type\\\": \\\"str\\\"}, \\\"coworker\\\": \\\"Data Quality Analyst\\\"}\"\u001b[00m\n",
+            "\u001b[95m## Tool Output:\u001b[00m \u001b[92m\n",
+            "\n",
+            "I encountered an error while trying to use the tool. This was the error: Arguments validation failed: 2 validation errors for DelegateWorkToolSchema\n",
+            "task\n",
+            "  Input should be a valid string [type=string_type, input_value={'description': \"Perform ...ySize'.\", 'type': 'str'}, input_type=dict]\n",
+            "    For further information visit https://errors.pydantic.dev/2.11/v/string_type\n",
+            "context\n",
+            "  Input should be a valid string [type=string_type, input_value={'description': \"The goal...s made.\", 'type': 'str'}, input_type=dict]\n",
+            "    For further information visit https://errors.pydantic.dev/2.11/v/string_type.\n",
+            " Tool Delegate work to coworker accepts these inputs: Tool Name: Delegate work to coworker\n",
+            "Tool Arguments: {'task': {'description': 'The task to delegate', 'type': 'str'}, 'context': {'description': 'The context for the task', 'type': 'str'}, 'coworker': {'description': 'The role/name of the coworker to delegate to', 'type': 'str'}}\n",
+            "Tool Description: Delegate a specific task to one of the following coworkers: Data Quality Analyst, Statistical Analyst\n",
+            "The input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolutely everything you know, don't reference things but instead explain them..\n",
+            "Moving on then. I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. When responding, I must use the following format:\n",
+            "\n",
+            "```\n",
+            "Thought: you should always think about what to do\n",
+            "Action: the action to take, should be one of [Delegate work to coworker, Ask question to coworker]\n",
+            "Action Input: the input to the action, dictionary enclosed in curly braces\n",
+            "Observation: the result of the action\n",
+            "```\n",
+            "This Thought/Action/Action Input/Result can repeat N times. Once I know the final answer, I must return the following format:\n",
+            "\n",
+            "```\n",
+            "Thought: I now can give a great answer\n",
+            "Final Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n",
+            "\n",
+            "```\u001b[00m\n",
             "\n",
             "\n",
             "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mPredictive Modeling Advisor\u001b[00m\n",
             "\u001b[95m## Final Answer:\u001b[00m \u001b[92m\n",
-            "['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Name', 'Cabin']\u001b[00m\n",
+            "['Survived', 'Pclass', 'Sex', 'Age', 'Fare', 'Embarked', 'FamilySize', 'Title']\u001b[00m\n",
             "\n",
             "\n"
           ]
@@ -1731,43 +1905,39 @@
           "data": {
             "text/plain": [
               "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "└── \u001b[1;33m📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\u001b[0m\n",
+              "└── \u001b[1;33m📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\u001b[0m\n",
               "    \u001b[37m   Status: \u001b[0m\u001b[2;33mExecuting Task...\u001b[0m\n",
               "    ├── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mPredictive Modeling Advisor\u001b[0m\n",
               "    │   \u001b[37m    Status: \u001b[0m\u001b[1;32mIn Progress\u001b[0m\n",
-              "    ├── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
-              "    │   \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "    └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mPredictive Modeling Advisor\u001b[0m\n",
               "        \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n"
             ],
             "text/html": [
               "
🚀 Crew: crew\n",
-              "├── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
+              "├── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
               "│      Assigned to: Data Quality Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Data Quality Analyst\n",
               "│           Status: ✅ Completed\n",
-              "├── 📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\n",
+              "├── 📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\n",
               "│      Assigned to: Statistical Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Statistical Analyst\n",
               "│           Status: ✅ Completed\n",
-              "└── 📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\n",
+              "└── 📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\n",
               "       Status: Executing Task...\n",
               "    ├── 🤖 Agent: Predictive Modeling Advisor\n",
               "    │       Status: In Progress\n",
-              "    ├── 🤖 Agent: Data Quality Analyst\n",
-              "    │       Status: ✅ Completed\n",
               "    └── 🤖 Agent: Predictive Modeling Advisor\n",
               "            Status: ✅ Completed\n",
               "
\n"
@@ -1793,45 +1963,41 @@
           "data": {
             "text/plain": [
               "\u001b[1;36m🚀 Crew: \u001b[0m\u001b[1;36mcrew\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "├── \u001b[1;32m📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\u001b[0m\n",
+              "├── \u001b[1;32m📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\u001b[0m\n",
               "│   \u001b[37m   Assigned to: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "│   \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "│   └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mStatistical Analyst\u001b[0m\n",
               "│       \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
-              "└── \u001b[1;32m📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\u001b[0m\n",
+              "└── \u001b[1;32m📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\u001b[0m\n",
               "    \u001b[37m   Assigned to: \u001b[0m\u001b[32mPredictive Modeling Advisor\u001b[0m\n",
               "    \u001b[37m   Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "    ├── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mPredictive Modeling Advisor\u001b[0m\n",
               "    │   \u001b[37m    Status: \u001b[0m\u001b[1;32mIn Progress\u001b[0m\n",
-              "    ├── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mData Quality Analyst\u001b[0m\n",
-              "    │   \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n",
               "    └── \u001b[1;32m🤖 Agent: \u001b[0m\u001b[32mPredictive Modeling Advisor\u001b[0m\n",
               "        \u001b[37m    Status: \u001b[0m\u001b[1;32m✅ Completed\u001b[0m\n"
             ],
             "text/html": [
               "
🚀 Crew: crew\n",
-              "├── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n",
+              "├── 📋 Task: 27b3e8e5-4f73-4516-8113-ca44b6419eb4\n",
               "│      Assigned to: Data Quality Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Data Quality Analyst\n",
               "│           Status: ✅ Completed\n",
-              "├── 📋 Task: 8eedb141-4888-4153-adab-2eefd60cf4ad\n",
+              "├── 📋 Task: 133ee44e-4b91-4802-8f23-e5d38d410e32\n",
               "│      Assigned to: Statistical Analyst\n",
               "│      Status: ✅ Completed\n",
               "│   └── 🤖 Agent: Statistical Analyst\n",
               "│           Status: ✅ Completed\n",
-              "└── 📋 Task: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\n",
+              "└── 📋 Task: 173c49d4-6975-4241-8596-c7daf5293e5e\n",
               "       Assigned to: Predictive Modeling Advisor\n",
               "       Status: ✅ Completed\n",
               "    ├── 🤖 Agent: Predictive Modeling Advisor\n",
               "    │       Status: In Progress\n",
-              "    ├── 🤖 Agent: Data Quality Analyst\n",
-              "    │       Status: ✅ Completed\n",
               "    └── 🤖 Agent: Predictive Modeling Advisor\n",
               "            Status: ✅ Completed\n",
               "
\n"
@@ -1846,7 +2012,7 @@
               "\u001b[32m╭─\u001b[0m\u001b[32m───────────────────────────────────────────────\u001b[0m\u001b[32m Task Completion \u001b[0m\u001b[32m───────────────────────────────────────────────\u001b[0m\u001b[32m─╮\u001b[0m\n",
               "\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m  \u001b[1;32mTask Completed\u001b[0m                                                                                                 \u001b[32m│\u001b[0m\n",
-              "\u001b[32m│\u001b[0m  \u001b[37mName: \u001b[0m\u001b[32maeb9dae7-a36c-4e1f-a4f7-968f7e6e0145\u001b[0m                                                                     \u001b[32m│\u001b[0m\n",
+              "\u001b[32m│\u001b[0m  \u001b[37mName: \u001b[0m\u001b[32m173c49d4-6975-4241-8596-c7daf5293e5e\u001b[0m                                                                     \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m  \u001b[37mAgent: \u001b[0m\u001b[32mPredictive Modeling Advisor\u001b[0m                                                                             \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n",
@@ -1856,7 +2022,7 @@
               "
╭──────────────────────────────────────────────── Task Completion ────────────────────────────────────────────────╮\n",
               "│                                                                                                                 │\n",
               "│  Task Completed                                                                                                 │\n",
-              "│  Name: aeb9dae7-a36c-4e1f-a4f7-968f7e6e0145                                                                     │\n",
+              "│  Name: 173c49d4-6975-4241-8596-c7daf5293e5e                                                                     │\n",
               "│  Agent: Predictive Modeling Advisor                                                                             │\n",
               "│                                                                                                                 │\n",
               "│                                                                                                                 │\n",
@@ -1887,7 +2053,7 @@
               "\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m  \u001b[1;32mCrew Execution Completed\u001b[0m                                                                                       \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m  \u001b[37mName: \u001b[0m\u001b[32mcrew\u001b[0m                                                                                                     \u001b[32m│\u001b[0m\n",
-              "\u001b[32m│\u001b[0m  \u001b[37mID: \u001b[0m\u001b[32mfb25122b-a197-4be3-98e2-7187488389ea\u001b[0m                                                                       \u001b[32m│\u001b[0m\n",
+              "\u001b[32m│\u001b[0m  \u001b[37mID: \u001b[0m\u001b[32m29f787f1-e6ba-4749-8b57-97e51a2c4aa5\u001b[0m                                                                       \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n",
               "\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n",
               "\u001b[32m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
@@ -1897,7 +2063,7 @@
               "│                                                                                                                 │\n",
               "│  Crew Execution Completed                                                                                       │\n",
               "│  Name: crew                                                                                                     │\n",
-              "│  ID: fb25122b-a197-4be3-98e2-7187488389ea                                                                       │\n",
+              "│  ID: 29f787f1-e6ba-4749-8b57-97e51a2c4aa5                                                                       │\n",
               "│                                                                                                                 │\n",
               "│                                                                                                                 │\n",
               "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
@@ -1925,7 +2091,7 @@
           "text": [
             "\n",
             "Crew Output:\n",
-            "['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Name', 'Cabin']\n"
+            "['Survived', 'Pclass', 'Sex', 'Age', 'Fare', 'Embarked', 'FamilySize', 'Title']\n"
           ]
         }
       ]