What's included

• Outcomes(Predictive report about the data forecast)

  1️⃣ Data Collection & Preprocessing Cleaned, structured data with missing values handled, feature engineering applied, and ready for modeling. 2️⃣ Exploratory Data Analysis (EDA) Insights into data distribution, trends, and key correlations using statistical analysis and visualizations. 3️⃣ Predictive Model Development Implementation of Regression, Classification, or Time Series models based on the business use case. 4️⃣ Model Performance Evaluation Assessment using accuracy, precision, recall, RMSE, MSE, R² score, or AUC-ROC to ensure reliability. 5️⃣ Feature Importance & Business Insights Identifying key factors driving predictions using SHAP values, correlation analysis, and interpretability techniques. 6️⃣ Model Optimization & Tuning Hyperparameter tuning using GridSearchCV, RandomizedSearchCV, or AutoML for improved accuracy. 7️⃣ Deployment & Integration (If Required) Deploying models using Streamlit, Flask, or API-based integration for real-time predictions. 8️⃣ Final Report & Recommendations Comprehensive documentation and PowerPoint presentation with findings, predictions, and business recommendations.