What's included

• Outcomes(Processed data with insights and modelling)

  1️⃣ Data Preparation & Preprocessing Cleaned and structured data with missing values handled, feature engineering applied, and ready for modeling. 2️⃣ Model Selection & Development Implementation of suitable machine learning models (Regression, Classification, Clustering, etc.) based on data and business needs. 3️⃣ Hyperparameter Tuning & Optimization Fine-tuned models using GridSearch, RandomizedSearch, or other optimization techniques for better accuracy. 4️⃣ Performance Evaluation Report Detailed metrics like Accuracy, Precision, Recall, RMSE, and ROC curves with clear interpretations. 5️⃣ Feature Importance & Interpretability Insights into key features driving predictions using SHAP values, permutation importance, or correlation analysis. 6️⃣ Model Deployment (If Required) Deployment of the trained model using Streamlit, Flask, or FastAPI for real-time usage. 7️⃣ Final Report & Documentation A structured report summarizing methodology, results, and recommendations, along with a PowerPoint presentation.