In this project i did time-series forecasting to forecast store sales on data. Linear regression, XGBoost, and LightGBM models are used to forecast total sales for each store.
Predicted customer churn based on machine learning Classification using the Random Forest algorithm and SVM XGboost finded log loss for models and compared. Churn and survival times using survival analysis dataset exploration.
Segemented the Customers based on RFM so that the company can target its customers efficiently. K-Means Clustering with 3 Cluster Ids and Hierarchical Clustering with 3 Cluster Labels. Ploted the results, used linkages. Elbow Curve to get the right number of Clusters.
Classify Yelp Reviews into 1 star or 5 star categories based off the text content in the reviews using Natural Language Processing Project, EDA Using Text Processing and MultinomialNB. predict product score based on the comments provided for that product.
Created the database and its table in MYSQL. Analyze data using Aggregate Functions. Run complex queries using SQL logical operators and string functions. Design and implement complex database schemas. Used SQL Joins and extracted information.
Analyzied Company's Sales data. Created and used DAX measures for powerful insights. Build a sales dashboard page in POWER-BI. Publishing and sharing the dashboard with the audience. Updating data and refreshing reports.
