xploratory Data Analysis- Definition of the problem (as per given problem statement with additional views) – Observations on the shape of data, data types of various attributes, missing values, statistical summary. – Univariate Analysis (boxplots, distribution plots for important variables) – Bivariate Analysis – Insights based on EDA5Data pre-processing- Prepare the data for analysis – Missing value Treatment – Outlier treatment – Ensure no data leakage4Model building- Build atleast 6 regression models (Using linear regression, decision trees, random forest, bagging regressor and boosting methods) – You can choose not to build XGBoost if you are facing issues with the installation7Choose to tune 3 models- Choose atleast 3 best performing models among all the models built previously to further tune them to improve the performance – Mention reasons for the choices made2Hyperparameter tuning- Tune the chosen models wrt the metric of interest – Check the performance of the tuned models6Model Performances- Compare performances of the tuned models and choose a final model (with reasoning) – Check the performance of the best model on the test set4Productionize the model- Productionize the final model using pipelines2Business Insights and Conclusions- Business Insights and Conclusions
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