Objective : To build different clusters to segment customers and analyze them Outcome : Successfully classified the customers into two groups to uniquely identify customers Nature Key skills : Data Cleaning, EDA, K-means Clustering, Hierarchical Clustering, Dimensionality reduction - Principal component Analysis (PCA)

Objective : To predict number of reactions, the user received on social media platform Outcome : A predictive Regression model developed which provides the number of reactions received by the user in social media Key skills : Data Cleaning, EDA, Statistical analysis, Linear Regression Analysis

Objective : To predict the customer churn rate in a telecom company Outcome : Developed a machine learning model, capable in predicting customer churn based on the customers data available with high accuracy followed by High F1 score Key skills : Data Cleaning, Exploratory Data Analysis(EDA), Statistical analysis, Logistic Regression, Decision Tree, Bagging and Boosting Techniques, Hyperparameter Tuning & SMOTE (handling imbalanced dataset)

Objective : To predict the customer churn rate in a telecom company
Outcome : Developed a machine learning model, capable in predicting customer churn based on the customers data available with high accuracy followed by High F1 score

Online Shopper’s Intentions – Clustering Technique Problem
Objective : To build different clusters to segment customers and analyze them
Outcome : Successfully classified the customers into two groups to uniquely identify customers Nature
Key skills : Data Cleaning, EDA, K-means Clustering, Hierarchical Clustering, Dimensionality reduction - Principal component Analysis (PCA)

Objective : To predict number of reactions, the user received on social media platform
Outcome : A predictive Regression model developed which provides the number of reactions received by the user in social media Key skills : Data Cleaning, EDA, Statistical analysis, Linear Regression Analysis