To predict the Bike Rental Count on daily basis

Role: Exploratory Data Analysis, Model Building and Evaluation.

To Predict the income level of the employees using XgBoost, Random Forest Model. This dataset has 30000 records. Data cleaning and analysis was done and model was built with an accuracy of 86.5% ,84.7% respectively.

Role: Data Cleaning,Data Manipulation, Model Building

Role: Data Cleaning, Data Manipulation, Model Building

To Predict customer Loan Repayments using XgBoost Model. This data has 100000 records. This data consists of columns like Credit Score , Annual Income , Current Credit Balance etc. Model was built with an accuracy of 78.4%.

To Predict employee attrition rate using a logistic regression. This data set consists of 4000 records.

Data was analyzed in model various aspects and a logistic regression model was built with an accuracy of 85%.

Role: Data Manipulation ,Visualization and building appropriate models for predicting.

Data for the cars manufactured in the last 15 years has been collected. Analysis is done to find out insights with respect to Mileage, Brand, Engine Capacity, Fuel_Type. A Linear Regression Model was built with an accuracy of 79%.