PHASE 1

Step	Task	Purpose
1	Load & preview data	Sanity check + get quick structure
2	Basic Info & Nulls	Look for missing data, dtypes
3	Column-by-column deep dive	Categorical vs numerical vs IDs
4	Correlation matrix	Spot potential relationships
5	Visual exploration	Univariate, bivariate, outliers
6	Target distribution	Check regression suitability
7	Feature transformation plan	Scaling, encoding, outliers, etc.
8	Final preprocessing design	Pipeline or manual steps

PHASE 2

Step	Task	Purpose
0	Train–Test Split	Prevent data leakage by splitting before preprocessing
1	Encode Categorical Variables	Convert categorical data into numerical format for models
2!	Drop/Engineer Features	Remove irrelevant features, create new mea ningful ones
3	Scale Numerical Features	Standardize/normalize numeric data for better model performance
4!	Handle Outliers (Optional)	Treat extreme values that may distort model training
5	Dimensionality Reduction (Optional)	Reduce feature space using PCA for noise removal or visualization
6	Finalize Processed Dataset	Ensure X_train/X_test/y_train/y_test are clean and ready
7	Build Preprocessing Pipeline	Use Pipeline + ColumnTransformer for reproducible preprocessing

PHASE 3

Step	Task	Purpose
0	Wire up y_train	Define target; keep X_train raw so the pipeline handles all preprocessing per fold
1	Define CV + Metrics	Use 5-Fold CV with RMSE/MAE/R² for apples-to-apples comparisons
2	Baselines (Dummy, Linear)	Establish performance floor and a simple linear benchmark
3	Regularized Linear (Ridge/Lasso/EN)	Handle noise/collinearity; check if linear models are enough
4	Trees & Boosting	Try RandomForest / GradientBoosting (optionally XGBoost/LightGBM later)
5	Cross-validate consistently	Evaluate each model in a Pipeline(preprocessor → model) to avoid leakage
6	Hyperparameter Tuning (Optional)	RandomizedSearchCV on top performers with tight, sensible ranges
7	Refit Best on Full Train	Fit the winning pipeline on all training data
8	Diagnostics (Optional)	Residuals, feature importance, learning curves
9	Save Best Pipeline	Persist the single preprocessor+model pipeline for later test eval & deployment

✅ Phase 0 — Project Setup & GitHub Init Create project directory and environment (conda, Jupyter)

Initialize Git repository and push to GitHub

Set up basic folder structure and README

Save raw dataset to data/raw/

📦 Outcome: Clean setup, project versioning started, README and folders ready

🔎 Phase 1 — EDA + Initial Understanding Load dataset, explore columns, understand datatypes

Visualize key features (distribution, correlation, outliers)

Understand missing values, class imbalance, etc.

Label encoding or quick mapping (e.g. 'Yes' → 1, 'No' → 0)

📦 Outcome: Deep understanding of the data, hypothesis ready, bad columns marked

🧹 Phase 2 — Data Preprocessing Handle missing values (drop, impute, custom methods)

Encode categorical features (Label/One-hot/Ordinal)

Scale/normalize numerical data

Drop irrelevant/noisy features manually

Optional: PCA or dimensionality reduction for pruning

Save cleaned dataset to data/processed/

🔧 We’ll design preprocessing pipeline here and keep it reusable via src/preprocessing.py.

📦 Outcome: Cleaned, encoded, scaled dataset → ready for ML/DL

🤖 Phase 3 — ML Modeling Train baseline models (LogReg, Decision Trees, Random Forest)

Train advanced models (SVM, XGBoost, LightGBM)

Hyperparameter tuning (GridSearchCV, RandomizedSearchCV)

Metric comparison: Accuracy, Precision, Recall, F1, AUC-ROC

Save best model (joblib, pickle)

Visualize confusion matrix, ROC curves, etc.

📦 Outcome: Best ML model with evaluation results and saved weights

🧠 Phase 4 — Deep Learning (MLP) Train a Deep Neural Net (MLP) using TensorFlow/Keras

Architecture tuning (layers, dropout, activations)

Compare with ML results

Optional: Add batch normalization, weight initialization, callbacks

Save model (.h5 or SavedModel format)

📦 Outcome: Working DNN for tabular data with metric comparison

🔍 Phase 5 — Interpretability & Explainability Feature importance (for tree-based models)

SHAP, LIME or ELI5 for DL model explainability

Compare which features affect prediction most

📦 Outcome: Interpretability plots + explanations for final report

☁️ Phase 6 — Deployment (Optional/Bonus) Convert notebook code to Python scripts/modules

Build simple CLI or Flask web app

Push to HuggingFace Spaces or Render (free tier)

Add usage guide in README

📦 Outcome: Minimal deployable project or API, usable by others

📘 Phase 7 — Final Docs + README Polish Clean and complete README.md

Add model performance charts

Final cleanup, remove junk notebooks, export PDFs

📦 Outcome: Shareable GitHub project with clarity and polish

🛠 Tools & Tech Stack Python, Pandas, NumPy, Matplotlib, Seaborn

Scikit-learn, XGBoost, LightGBM

TensorFlow/Keras

SHAP, ELI5, LIME

Jupyter Notebook, VS Code

Git, GitHub