Welcome to my very first machine learning practice project! 🎉
The project was focused on predicting California housing prices, this project was an incredible learning experience where I had the opportunity to perform all the essential steps of a data science project, including data extraction, EDA, preprocessing, modeling, gaining insights from model prediction.
I also went a step further and created a front-end flask webapp to make the trained model available on the web. This gave me an opportunity to showcase my skills in full-stack development and put my machine learning model into production.
Through this project, I was able to gain valuable insights into the data and build a model that accurately predicted housing prices in California. I used various machine learning techniques, including linear regression, decision trees, and random forests, Gradient Boosted Regression to build the most effective model.
This project marks an important milestone in my data science journey, and I am thrilled to finally be able to apply my theoretical knowledge in practice and learn so many new technologies and techniques along the way, and I am excited to continue learning and growing in this field.
I am grateful for this opportunity and excited to see what the future holds! 🌟
I have shared the Complete Development process of this project using Blogs for each step of the process and you can see them by clicking on the corresponding topic links.
You can also View the Project on GitHub
Overview
This Project covers all the necessary steps to complete the Machine Learning Task of Predicting the Housing Prices on California Housing Dataset available on scikit-learn. I performed the following steps for successfully creating a model for house price prediction:
Import libraries.
Import Dataset from scikit-learn.
Understanding the given Description of Data and the problem Statement
Take a look at different Inputs and details available with dataset.
Storing the obtained dataset into a Pandas Data Frame.
Getting a closer Look at obtained Data.
Exploring different Statistics of the Data (Summary and Distributions)
Looking at Correlations (between individual features and between Input features and Target)
Geospatial Data / Coordinates - Longitude and Lattitude features
Dealing with Duplicate and Null (NaN) values
Dealing with Categorical features (e.g. Dummy coding)
Dealing with Outlier values
Visualization (Boxplots)
Using IQR
Using Z-Score
Separating Target and Input Features
Target feature Normalization (Plots and Tests)
Splitting Dataset into train and test sets
Feature Scaling (Feature Transformation)
Specifying Evaluation Metric R squared (using Cross-Validation)
Model Training - trying multiple models and hyperparameters:
Linear Regression
Polynomial Regression
Ridge Regression
Decision Trees Regressor
Random Forests Regressor
Gradient Boosted Regressor
eXtreme Gradient Boosting (XGBoost) Regressor
Support Vector Regressor
Model Selection (by comparing evaluation metrics)
Learn Feature Importance and Relations
Prediction
Exporting the trained model to be used for later predictions. (By storing model object as byte file - Pickling)
Creating a Flask App for deploying model on the web.
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