This repository contains code for a machine learning project aimed at predicting insurance charges using Python and various libraries such as NumPy, pandas, Matplotlib, Seaborn, and scikit-learn. The project involves data preprocessing, exploratory data analysis (EDA), model building, and evaluation.
To run the code in this repository, you need to have the following libraries installed:
- NumPy
- pandas
- Matplotlib
- Seaborn
- scikit-learn
- statsmodels
You can install these libraries using pip: Description The main script insurance_charges_prediction.py performs the following tasks:
Imports necessary libraries: NumPy, pandas, Matplotlib, Seaborn. Reads the insurance dataset from a CSV file. Conducts data preprocessing tasks such as handling missing values, encoding categorical variables, and removing outliers. Performs exploratory data analysis (EDA) using visualizations. Builds a linear regression model to predict insurance charges. Evaluates the model's performance using metrics such as R-squared, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).
To perform exploratory data analysis (EDA) on the given dataset, we'll examine each variable individually and explore relationships between variables. Here's a basic outline of the steps we can take:
Summary Statistics: Calculate basic statistics like mean, median, standard deviation, minimum, and maximum for numerical variables. Distribution Analysis: Visualize the distribution of numerical variables using histograms and box plots. Categorical Variables: Explore the distribution of categorical variables using bar plots. Correlation Analysis: Examine the correlation between numerical variables using a correlation matrix and heatmap. Relationships with Target Variable: Explore how each variable relates to the target variable ('charges') using scatter plots and box plots.