Merge pull request #1516 from RB137/patch-2

Added README.md file inside Advanced-house-prediction model section

Author: sanjay-kv

Prediction Models/Advanced House Price Predictions/README.md

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+# Advanced House Price Prediction Model
+
+This project focuses on predicting house prices using advanced machine learning techniques. The model is trained on a dataset containing various features related to houses (e.g., square footage, number of rooms, location) to estimate the selling price.
+
+## Features
+
+- *Preprocessing*: Handles missing values, outliers, and categorical data.
+- *Feature Engineering*: Adds relevant new features to improve model performance.
+- *Modeling*: Uses multiple models including Linear Regression, Decision Trees, Random Forest, and Gradient Boosting.
+- *Evaluation*: Assesses the model's performance using metrics like RMSE (Root Mean Squared Error) and R² (coefficient of determination).
+
+## Dataset
+
+The dataset contains features such as:
+
+- *Lot Area*: Size of the lot in square feet
+- *Year Built*: Year the house was constructed
+- *Overall Quality*: Material and finish quality
+- *Total Rooms*: Number of rooms excluding bathrooms
+- *Neighborhood*: The physical location of the property
+- *Sale Price*: The target variable for prediction
+
+## Setup
+
+1. Clone the repository:
+   bash
+   git clone https://github.com/recodehive/house-price-prediction.git
+2. Navigate to the project directory:
+   bash
+   cd house-price-prediction
+3. Install dependencies:
+   bash
+   pip install -r requirements.txt
+4. Run the model:
+   bash
+   python main.py
+
+## Model Training
+
+The model uses the following steps:
+
+1. Data Preprocessing: Imputing missing values, scaling numerical features, encoding categorical variables.
+2. Feature Selection: Selecting the most important features to reduce overfitting.
+3. Training: Training multiple models to compare their performances.
+3. Evaluation: Checking accuracy using cross-validation and other metrics.
+
+## Results
+
+The model achieved a RMSE of X and R² of Y on the test dataset. Gradient Boosting performed the best, followed by Random Forest.
+
+## Contribution
+
+Feel free to fork the repository and contribute! Submit pull requests with clear descriptions of the changes made.