This project focuses on applying machine learning techniques to a given dataset in order to predict a target variable. The following steps have been taken in this project:
Exploratory Data Analysis (EDA) - The first step involved understanding the data and its distribution. This helped in identifying any missing or incorrect values and also helped in understanding the relationship between different features and the target variable.
Feature Selection - The next step involved selecting the most important features from the data to use in the machine learning models. This helped in reducing the dimensionality of the data and also helped in improving the accuracy of the models.
Model Implementation - After the data was cleaned and prepared, different machine learning models were implemented. These models included Decision Trees, ADABoost, XGBoost, and Random Forest. The models were trained on the data and their performance was evaluated using metrics like accuracy, precision
To run the code, you will need the following packages installed:
numpy pandas matplotlib seaborn scikit-learn xgboost
In this project, various machine learning models were implemented and their performance was evaluated. Based on the evaluation, the best model was selected and used for prediction. This project serves as a good starting point for anyone looking to implement machine learning techniques for a similar problem.
