L2T14 - Capstone Project Linear Regression Analysis

Overview

This capstone project involves performing linear regression analysis on the Boston housing dataset (boston.csv). The goal is to build a predictive model for housing prices using independent variables such as the average number of rooms (rm) and the proportion of the population with a low status (lstat). The project includes data cleaning, exploratory data analysis (EDA), model building, and evaluation.

Instructions

Setup and Installation

1. Clone the Repository

   • Clone this repository to your local machine:

     git clone https://github.com/vswapna3202/L2T14.git
     

2. Navigate to the Project's Folder

   • Navigate to the folder containing the project:

     cd L2T14 or cd <your-task-folder>
     

3. Install Dependencies

   • Ensure you have Python installed on your system. You can download it from the official Python website.
   • Install Jupyter Notebook to run the provided notebook:

     pip install notebook
     

   • Install necessary libraries such as pandas, numpy, matplotlib, and scikit-learn:

     pip install pandas numpy matplotlib scikit-learn
     

Project Workflow

1. Reading and Preparing the Data

   • Read the boston.csv file into the Jupyter notebook Capstone Linear Regression.ipynb.
   • Clean and prepare the dataset if necessary.

2. Exploratory Data Analysis (EDA)

   • Perform EDA to gain insights into the dataset.
   • Visualize the distributions of the dependent variable and independent variables.
   • Identify any patterns or trends in the data.

3. Linear Regression Model

   • Use the following independent variables: rm (average number of rooms) and lstat (proportion of the population with low status).
   • Split the dataset into the independent variables and the single dependent variable.
   • Generate plots to explore the relationships between the independent variables and the dependent variable.

4. Model Building and Evaluation

   • Split the data into training and test sets using a split ratio of 75:25.
   • Build a multiple linear regression model using the training set with all the independent variables.
   • Print out the intercept and coefficients of the trained model.
   • Generate predictions for the test set.
   • Evaluate the model's performance by computing the mean squared error (MSE) or root mean squared error (RMSE) on the test set.
   • Generate an error plot to visualize the differences between the predicted and actual values in the test set.
   • Print the coefficients and interpret them within the context of the median value prediction.

5. Summary

   • Summarize the findings from the analysis, including insights from the exploratory data analysis, model performance, and any notable observations within the notebook.

Running the Notebook

• Start Jupyter Notebook:

jupyter notebook

• Open the Jupyter notebook Capstone Linear Regression.ipynb.

Dataset

• boston.csv: Contains data on various attributes of houses in Boston, including features like average number of rooms (rm), proportion of population with low status (lstat), and median value of owner-occupied homes (medv). Source