These projects were completed as the part of the final project of my Quantum Learnings Machine Learning Internship Training Program.
Notebook: Jupyter Notebook File
PDF Report: LaTex Generated Report
These are the 4 projects that are covered in this notebook.
1. Fraud Dataset (Predicting whether the customers will be a defaulter or not)
a. Using SVM Classification (93.04 % Accuracy Score)
b. Using Custom threshold for strongest filtering (86.081 % Accuracy Score)
c. Using K Nearest Neighbour Classification (93.41 % Accuracy Score)
2. Diamond Price Prediction (Predicting the prices of diamonds.)
a. Using Multiple Linear Regression (88.52 % Accuracy Score)
b. Using Artificial Neural Networks (94.3 % Accuracy Score)
3. Company Attrition Data (Whether the employee would leave the company or not)
a. Using K Nearest Neighbour Classification (96.83 % Accuracy Score)
4. House Price Prediction (Predicting house prices)
a. Using Random Forest Regression (100 % Accuracy Score)