House price Prediction Project Added

docs/ML/projects/house_price_prediction.md

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+# House Price Prediction
+This project utilizes machine learning techniques to predict house prices based on various features such as location, size, and amenities. By analyzing historical data, the model provides accurate price estimations for real estate properties.
+
+# Project Structure 
+The project is organized into the following key files:
+* [Data Cleaning.ipynb](https://github.com/shrek-28/house-price-prediction/blob/main/Data%20Cleaning.ipynb): This notebook contains steps for cleaning and preprocessing the dataset. It addresses issues such as missing values, categorical variables, and outliers to prepare the data for analysis.
+* [Predictive Analytics.ipynb](https://github.com/shrek-28/house-price-prediction/blob/main/Predictive%20Analytics.ipynb): This notebook covers the predictive modeling aspect of the project. It includes the implementation of various regression models, model evaluation metrics, and the selection of the best-performing model.
+
+# Prerequisites
+To run this project, you need to have the following software and libraries installed:
+
+* Python 3.x
+* Jupyter Notebook
+* Pandas
+* NumPy
+* Scikit-learn
+* Matplotlib
+* Seaborn
+
+You can install the required libraries using the following command:
+
+```
+pip install pandas numpy scikit-learn matplotlib seaborn
+```
+
+# Getting Started
+* Clone the repository:
+```
+git clone https://github.com/yourusername/house-price-prediction.git
+cd house-price-prediction
+```
+* Run the notebooks:
+- Open the [Data Cleaning.ipynb](https://github.com/shrek-28/house-price-prediction/blob/main/Data%20Cleaning.ipynb) notebook in Jupyter Notebook and execute the cells to clean and preprocess the data.
+- Proceed to the [Predictive Analytics.ipynb](https://github.com/shrek-28/house-price-prediction/blob/main/Predictive%20Analytics.ipynb) notebook to explore the data, build predictive models, and evaluate their performance.
+
+# Project Workflow
+* Data Cleaning: Handle missing values, encode categorical variables, and remove outliers to ensure the data is ready for analysis.
+* Exploratory Data Analysis (EDA): Visualize key relationships and distributions in the data to gain insights.
+* Modeling: Implement various regression models, including Linear Regression and Random Forests, to predict house prices.
+* Model Evaluation: Assess model performance using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
+* Model Selection: Choose the best-performing model based on evaluation metrics for deployment or further analysis.
+
+# Results
+The final model demonstrates the capability to predict house prices with a high degree of accuracy, making it a valuable tool for real estate market analysis and decision-making
+
+# Future Work
+Potential improvements for the project include:
+* Incorporating more advanced models like Gradient Boosting or XGBoost.
+* Enhancing feature engineering to include more relevant variables.
+* Deploying the model using a web framework like Flask for real-time predictions.
+
+Disclaimer: This was done as part of the project work for the Udemy Course [Python for Data Science and Machine Learning Masterclass](https://www.udemy.com/course-dashboard-redirect/?course_id=2769460)

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       link: https://github.com/Avdhesh-Varshney/AI-Code
-
-copyright: Copyright © 2024 Avdhesh Varshney
+  footer:
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+      - title: About
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+          - title: About Me
+            url: /about/
+          - title: Contact
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+            url: /docs/
+          - title: Tutorials
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