Kaggleproject

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KaggleProject/readme.md

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+![](UTA-DataScience-Logo.png)
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+# Santander Customer Satisfaction Prediction
+
+**By Subham Kalwar**
+
+* **One Sentence Summary:** An applied machine learning pipeline predicting customer dissatisfaction on a highly imbalanced dataset using a tuned, class-weighted Random Forest architecture. 
+
+## Overview
+
+* **Definition of the task:** The objective of this Kaggle challenge is to identify dissatisfied customers early in their relationship with Santander Bank. The dataset consists of over 370 anonymized tabular features. The evaluation metric is the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), which is necessary due to the extreme class imbalance in the target variable.
+* **My approach:** The problem was formulated as a binary classification task. Exploratory Data Analysis revealed a severe 96:4 split between satisfied and unsatisfied customers. To address this, I focused on data pruning (removing zero-variance noise) and utilized a Random Forest Classifier. The model was specifically tuned with balanced class weights to penalize the misclassification of the minority class. 
+* **Summary of the performance achieved:** The final tuned Random Forest model achieved a local Validation AUC of 0.8216. When evaluated against the unseen test data on Kaggle, it secured a Public Leaderboard AUC score of 0.79119, demonstrating a strong ability to generalize patterns without severe overfitting.
+
+## Summary of Work Done
+
+### Data
+
+* **Type:** * Input: CSV file containing integer and float features (anonymized, e.g., `var15`, `var38`).
+  * Output: Binary target column (`TARGET`), where `1` indicates an unsatisfied customer and `0` indicates a satisfied customer.
+* **Size:** * Training Data: ~76,020 instances with ~370 features.
+  * Testing Data: ~75,818 instances.
+* **Instances (Train/Validation Split):** * An 80/20 train/validation split was utilized. Crucially, a `stratify` parameter was applied to ensure the validation set maintained the exact 96:4 imbalance ratio present in the training data.
+
+#### Preprocessing / Clean up
+
+* **Variance Filtering:** Scanned the dataset for constant features (Standard Deviation = 0) and dropped them, reducing dimensionality and training overhead.
+* **Deduplication:** Dropped identical rows to prevent the model from overfitting on repeated data points.
+* **Imputation:** Handled missing/null values internally within the modeling pipeline. 
+
+#### Data Visualization
+
+* Extracted feature importances from the trained Random Forest model. Visualizations confirmed that specific features, notably `var15` (widely hypothesized to represent customer Age), carried the highest relative importance when determining customer satisfaction.
+
+### Problem Formulation
+
+* **Input / Output:** Input is a vector of preprocessed numerical features for a single customer. Output is a continuous probability from `0.0` to `1.0` representing the likelihood of dissatisfaction.
+* **Model:** * **Random Forest Classifier:** Selected for its robust handling of non-linear tabular data and its resistance to single-tree overfitting through ensemble voting. 
+* **Hyperparameters:**
+  * `n_estimators=500`: Increased tree count for a more stable, averaged prediction.
+  * `max_depth=15`: Pruned the trees to prevent them from memorizing the training data.
+  * `min_samples_split=10`: Forced broader generalizations at the leaf nodes.
+  * `class_weight='balanced'`: Automatically adjusted weights inversely proportional to class frequencies to combat the 96% majority class.
+
+### Training
+
+* **Hardware & Environment:** Trained primarily on Kaggle's cloud environment utilizing Python 3, `pandas`, `numpy`, and `scikit-learn`.
+* **Process:** Training was accelerated utilizing all available CPU cores (`n_jobs=-1`). The primary difficulty encountered was the model defaulting to predicting `0` for almost all rows due to the imbalance. This was resolved by implementing the `class_weight` parameter and evaluating via `.predict_proba()` rather than absolute `.predict()` classes.
+
+### Performance Comparison
+
+* **Key Performance Metric:** AUC-ROC (Area Under the Receiver Operating Characteristic Curve).
+* **Results:**
+  * Training Split AUC: ~0.90+
+  * Stratified Validation AUC: 0.8216
+  * Final Kaggle Public Leaderboard AUC: 0.79119
+
+### Conclusions
+
+* A standard Random Forest can successfully navigate highly imbalanced datasets if depth is strictly controlled and minority classes are mathematically weighted. The ~3% gap between validation and test scores indicates minimal overfitting and a healthy, generalized model.
+
+### Future Work
+
+* **Algorithm Shift:** Transition the baseline model to Gradient Boosting frameworks like XGBoost or LightGBM, which historically dominate this specific dataset.
+* **Feature Engineering:** Conduct deeper bivariate analysis to combine existing anonymized variables into more highly correlated composite features.
+
+## How to reproduce results
+
+### Overview of files in repository
+
+* `santander_rf_model.ipynb`: The primary Jupyter Notebook containing the end-to-end pipeline. Includes EDA, data cleaning, model training, validation scoring, and final CSV generation.
+* `submission.csv`: The final output file containing the test IDs and predicted probabilities, formatted for Kaggle scoring.
+* `README.md`: Project documentation and methodology summary.
+
+### Software Setup
+* Standard Data Science Python stack required:
+  * `pandas`
+  * `numpy`
+  * `scikit-learn`
+  * `matplotlib` / `seaborn` (for visualization)
+
+### Data
+* The raw dataset is hosted by Kaggle. You can download `train.csv`, `test.csv`, and `sample_submission.csv` directly from the [Santander Customer Satisfaction competition page](https://www.kaggle.com/c/santander-customer-satisfaction/data).
+
+### Training and Evaluation
+* Clone the repository and ensure the Kaggle data files are located in the same directory (or update the file paths in the notebook).
+* Run the `santander_rf_model.ipynb` notebook sequentially from top to bottom to clean the data, train the ensemble, evaluate the validation split, and generate a new `submission.csv`.