Nihal Habeeb’s Projects
HEALTH INSURANCE CROSS SELL PREDICTION
Cross selling is the process of offering an existing customer a product that is similar or compatible to the product that they already purchased. Acquiring a new customer is harder than retaining existing customers, which makes customer relationship a very important aspect for any business. Cross selling can be an effective method to strengthen the relationship with the customer while also boosting the revenue of the business. I am using a dataset containing information of health insurance policy holders (of past year) to predict whether they will be interested in the vehicle insurance of the company.
Project Overview
- All the features in the dataset (including Age, Annual Premium amount, Vehicle Age etc.) are carefully studied.
- The Response (Interested, Not interested) classes were highly imbalanced. SMOTE was used to balance them.
- Logistic Regression, Decision Tree Classifier, Random Forest Classifier and XGBoost Classifier were built.
- The performances of these models are evaluated and compared.
- Python libraries such as Pandas, Matplotlib, Seaborn, Numpy, Imbalanced-learn, Scikit-learn and XGBoost are used.
Access the complete project HERE
RENTAL BIKE DEMAND PREDICTION
For a bike renting system to smoothly function, it is necessary to provide a stable supply of rental bikes at any given point of time according to the demand. This requires having a good prediction of the bike demand at each hour. I am working with a dataset of bike rental counts in the city of Seoul, South Korea which contains historical data on date and weather information (Temperature, Humidity, Windspeed, Visibility, Dewpoint, Solar radiation, Snowfall, Rainfall).
Project Overview
- The distributions of the features as well as their relationship with the rented bike count is explored.
- Linear regression model is trained on the data to make predictions and its performance is evaluated.
- Decision Tree Regression model is trained for getting better predictions and this model’s performance is evaluated as well.
- Python libraries such as Matplotlib, Seaborn, Pandas and Scikit-learn are used.
Access the complete project HERE
Linear Regression Model - Predicted and Actual values
Decision Tree Regression Model - Predicted and Actual values
PLAYSTORE DATA ANALYSIS
Project Overview
- In this project, the distribution of applications in relation to categories and genres and whether they’re paid or free is explored.
- Information on the money spent by consumers buying applications, as well as the review activities are obtained.
- Derived conclusions can help app developers gain an understanding on how to capture the market.
- PostgreSQL and Tableau are the tools used.
Access the complete project HERE
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CONTACT
email: nihalmhabeeb@gmail.com
Twitter: @nihalmhabeeb