![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Analyst agents to auto-explore datasets"
+ ],
+ "metadata": {
+ "id": "LV3nP_sr3jMC"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Motivation**\n",
+ "\n",
+ "Data analysts live by joins. Combining multiple tables – customer details, sales figures, inventory levels – is fundamental to uncovering deeper business insights. But this process is fragile. The final analysis is only as strong as its weakest data link.\n",
+ "\n",
+ "Introduce just one dataset with quality issues into your sequence of joins, and the entire result becomes suspect. The more tables you bring into the mix, the higher the odds that at least one of them is flawed. Suddenly, that five-table join feels much riskier than a simple two-table lookup. This compounding risk underscores a crucial, often time-consuming step: rigorous data quality assessment before merging.\n",
+ "\n",
+ "So, how do we typically combat this risk? Before diving into complex joins or modeling, the diligent data analyst performs crucial exploratory data analysis (EDA) and quality checks on each input dataset. This isn't just a single step, but a well-trodden path involving several standard procedures.\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "9P93UoEe4OzO"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "WShWG64Du6tF"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install summarytools langchain-google-genai crewai -q"
+ ],
+ "metadata": {
+ "id": "O4_y09ytn-aK"
+ },
+ "execution_count": 5,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Establishing a Data Understanding Baseline**\n",
+ "\n",
+ "Before complex analysis, understanding each input dataset is crucial. While experienced analysts develop diverse techniques, a common baseline approach guides most initial data quality checks and exploration. This foundational process typically involves:\n",
+ "\n",
+ "1. Missing Values: Calculating the count and percentage of nulls per variable.\n",
+ "2. Continuous Distributions: Examining key statistics (mean, median, min, max, std dev) and outliers, often visualized with histograms.\n",
+ "3. Categorical Frequencies: Identifying unique values, their counts, and potential anomalies in categorical fields.\n",
+ "4. Trend analysis: Tracking key metrics over time (if applicable) to spot patterns or shifts.\n",
+ "5. etc.\n",
+ "\n",
+ "The example belows demonstrates using \"summarytools\" package in Python to summarize the famous titanic dataset."
+ ],
+ "metadata": {
+ "id": "69rF3egFtNAu"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "Tptn3IAGl513",
+ "outputId": "982aa408-d78a-45e7-8a60-fdc2c0a0753b"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "| No | \n", + "Variable | \n", + "Stats / Values | \n", + "Freqs / (% of Valid) | \n", + "Graph | \n", + "Missing | \n", + "
|---|---|---|---|---|---|
| 1 | \n", + "PassengerId [int64] | \n",
+ " Mean (sd) : 446.0 (257.4) min < med < max: 1.0 < 446.0 < 891.0 IQR (CV) : 445.0 (1.7) | \n",
+ " 891 distinct values | \n", + "0 (0.0%) | \n",
+ " |
| 2 | \n", + "Survived [int64] | \n",
+ " 1. 0 2. 1 | \n",
+ " 549 (61.6%) 342 (38.4%) | \n",
+ " 0 (0.0%) | \n",
+ " |
| 3 | \n", + "Pclass [int64] | \n",
+ " 1. 3 2. 1 3. 2 | \n",
+ " 491 (55.1%) 216 (24.2%) 184 (20.7%) | \n",
+ " 0 (0.0%) | \n",
+ " |
| 4 | \n", + "Name [object] | \n",
+ " 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 | \n",
+ " 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%) | \n",
+ " 0 (0.0%) | \n",
+ " |
| 5 | \n", + "Sex [object] | \n",
+ " 1. male 2. female | \n",
+ " 577 (64.8%) 314 (35.2%) | \n",
+ " 0 (0.0%) | \n",
+ " |
| 6 | \n", + "Age [float64] | \n",
+ " Mean (sd) : 29.7 (14.5) min < med < max: 0.4 < 28.0 < 80.0 IQR (CV) : 17.9 (2.0) | \n",
+ " 88 distinct values | \n", + "177 (19.9%) | \n",
+ " |
| 7 | \n", + "SibSp [int64] | \n",
+ " 1. 0 2. 1 3. 2 4. 4 5. 3 6. 8 7. 5 | \n",
+ " 608 (68.2%) 209 (23.5%) 28 (3.1%) 18 (2.0%) 16 (1.8%) 7 (0.8%) 5 (0.6%) | \n",
+ " 0 (0.0%) | \n",
+ " |
| 8 | \n", + "Parch [int64] | \n",
+ " 1. 0 2. 1 3. 2 4. 5 5. 3 6. 4 7. 6 | \n",
+ " 678 (76.1%) 118 (13.2%) 80 (9.0%) 5 (0.6%) 5 (0.6%) 4 (0.4%) 1 (0.1%) | \n",
+ " 0 (0.0%) | \n",
+ " |
| 9 | \n", + "Ticket [object] | \n",
+ " 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 | \n",
+ " 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%) | \n",
+ " 0 (0.0%) | \n",
+ " |
| 10 | \n", + "Fare [float64] | \n",
+ " Mean (sd) : 32.2 (49.7) min < med < max: 0.0 < 14.5 < 512.3 IQR (CV) : 23.1 (0.6) | \n",
+ " 248 distinct values | \n", + "0 (0.0%) | \n",
+ " |
| 11 | \n", + "Cabin [object] | \n",
+ " 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 | \n",
+ " 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%) | \n",
+ " 687 (77.1%) | \n",
+ " |
| 12 | \n", + "Embarked [object] | \n",
+ " 1. S 2. C 3. Q 4. nan | \n",
+ " 644 (72.3%) 168 (18.9%) 77 (8.6%) 2 (0.2%) | \n",
+ " 2 (0.2%) | \n",
+ "
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\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[92mData Quality Task:\n", + "Examine the dataset summary below and identify columns with significant data quality issues or concerns.\n", + "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", + "No Variable Stats / Values Freqs / (% of Valid) Graph Missing\n", + "1 PassengerId
🚀 Crew: crew\n", + "└── 📋 Task: 1a1b12c0-5c13-40bc-87fb-2111b26a0890\n", + " Status: Executing Task...\n", + " └── 🤖 Agent: Data Quality Analyst\n", + " Status: ✅ Completed\n", + "\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\n" + ], + "text/html": [ + "
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🚀 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", + "\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\n" + ], + "text/html": [ + "
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🤖 Agent: Predictive Modeling Advisor\n", + " Status: In Progress\n", + "\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\n" + ], + "text/html": [ + "
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🚀 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": [ + "
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🚀 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", + " └── 🤖 Agent: Statistical 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[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", + "\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", + "\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", + " └── \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", + "│ 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", + " └── 🤖 Agent: Statistical 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[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## 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", + "\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" + ] + }, + { + "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", + "└── \u001b[1;34m🧠 \u001b[0m\u001b[34mThinking...\u001b[0m\n" + ], + "text/html": [ + "
🤖 Agent: Statistical Analyst\n", + " Status: ✅ Completed\n", + "└── 🧠 Thinking...\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;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", + "\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## Final Answer:\u001b[00m \u001b[92m\n", + "['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Name', 'Cabin']\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", + " └── \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": [ + "
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\n", + "\n" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Crew Output:\n", + "['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Name', 'Cabin']\n" + ] + } + ] + } + ] +} \ No newline at end of file