Machine-Learning-Projects

These are my projects for machine learning. I use regression analysis, logistic regression, hypothesis testing, time series and differnt models to train my data.

Hypothesis Analysis on Marketing Campaign and Driving Miles

• Analyze the SFO and LAX data sets and determine if either marketing campaign was successful in raising the average miles driven per Uber driver.

Logistic Regression on Customers Transaction Prediction

• Use logistic regression to predict when customers are going to transact
• Determine the causes for a transaction
• Evaluated the performance the model

Logistic Regression on Employee Turnover Prediction

• Use logistic regression to predict when people are going to leave a company
• Determine the causes for attrition
• Evaluated the performance the model

Predict Salary by Lasso & Ridge Regularization

• Use regularization to predict salaries for a sports player
• Explain the output of the regularized models

Regression Analysis on Housing Price

• Remove/manipulate/transform features from the data set, remain only useful data
• Graphically and numerically describe model performance and find the relation between them
• Apply regression analysis techniques and EDA principles to find out what features will influence the rental price

Telecom Customer Churn Prediction By Using Different Machine Learning algorithms

• Trialed a list of different Machine Learning algorithms, such as Logistic Regression(with Lasso & Ridge), Decision Tree, KNN Classifier, and Random Forest Classifier, and Linear Regression to predict potential customer churn and customer life time value.
• Provided the best model that has achieved the highest AUC value with lowest MSE(Mean Squared Error).
• Contructed the particial dependece plot to discover how the most 6 importance features related to the customer churn.

Time Series Analysis Using ARIMA on Electro Data Prediction

• Implemented ARIMA model, analyzed 2 data sets to predict the values for the next 8 time periods and the subsequent 7 years (with confidence intervals), and make 3 observations about the data (i.e., describe its composition and characteristics).

Forecasting on Video CTR

• Using Moving Average, Exponential smoothing, AR and ARIMA model to forecast video CTR (click through rate)
• Select a performance measure for the model and pick the best performing model with lowest MSE.

Multiple Regression Analysis on Civilization VI Game Players' Active Days

• Determine the causes of active day
• Use multiple regression model to predict players' active day