These are my projects for machine learning. I used regression analysis, logistic regression, hypothesis testing, time series and differnt models with R to train my data and propose better decisions for the company.
Predict the selling prices of houses in the region: You are in market to buy 4 bedrooms, 2 baths and 2 storied houses with approx lot size of 5500 SFT in specific area. You would like to gather historical sales data and analyze for bidding the right price for the house.
Based on the housing sales history data provide the following:
• Comparative study of house sale in specific region
• Identify house price variation
Goal: To know more about what make the customer churn and predicting if he/she will churn or not. Data set: The data was downloaded from IBM Sample Data Sets for customer retention programs. You can download it from here: https://www.kaggle.com/blastchar/telco-customer- churn/download
The data set includes information about:
- Customers who left within the last month – the column is called Churn
- Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies
- Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
- Demographic info about customers – gender, age range, and if they have partners and dependents
Goal: Applied time series forecasting method
I selected the power consumption in telecommunication networks, which operates primarily in Pittsburgh. You can find the data source here: https://www.kaggle.com/apoorvabhide/energy- consumption-time-series-forecasting-in-r/#data
The data set includes information about:
- Datatime - Show power consumption in each timestamp and date.
- Power consumption in telecommunication networks - the volume that the power was used
Goal: To know more about what make the customer churn and predicting if he/she will churn or not.
Data set: The data was downloaded from IBM Sample Data Sets for customer retention programs. You can download it from here: https://www.kaggle.com/blastchar/telco-customer- churn/download
The data set includes information about:
- Customers who left within the last month – the column is called Churn Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies
- Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
- Demographic info about customers – gender, age range, and if they have partners and dependents
Goal: To know more about what make the customer churn and predicting if he/she will churn or not. Data set: The data was downloaded from IBM Sample Data Sets for customer retention programs. You can download it from here: https://www.kaggle.com/blastchar/telco-customer- churn/download
The data set includes information about:
- Customers who left within the last month – the column is called Churn Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies
- Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
- Demographic info about customers – gender, age range, and if they have partners and dependents