Update README.md

README.md

@@ -4,11 +4,21 @@ Kevadiya, Jenil, 22204227
 
 **Title - Laptop Price Prediction**
 
+Link to repository: 
+
 # Project Description
 
-A Machine Learning Model to predict laptop price based on user requirement. This project uses Random Forest Regression Model to train on a given dataset. Interactive GUI is developed using PyQt6. 
+A Machine Learning Model to predict laptop price based on user requirement. This project uses Random Forest Regression Model to train on a given dataset. Interactive GUI is developed using PyQt6.
+
+#Installation
+
+1. Clone the repository.
+2. Create a virtual environment: `python -m venv venv`
+3. Activate the virtual environment: `source venv/bin/activate` (Linux/Mac) or `venv\Scripts\activate` (Windows)
+4. Install dependencies: `pip install -r requirements.txt`
+5. Follow the steps in the Usage section.
 
-# Usage
+#Basic Usage
 
 Download this git repository or clone it to your system using following command:
 
@@ -18,7 +28,49 @@ Install required python packages from requirements.txt file using following comm
 
 ``pip install -r requirements.txt``
 
-Double click and run main.py file to use the prediction model.
+1. Run the app: `python src/main.py`
+2. Load data using the provided button.
+3. Explore laptop data in the displayed table.
+
+#Implementation of the Request
+# Implementation of the Requests
+
+1. Graphical User Interface (GUI) Components
+**Function:** main window, buttons, QcomboBox, Qslider, QcheckBox and QTextBrowser for the input sections.
+- **Contribution by [Other Student]:** Designed and implemented various UI components for improved aesthetics and usability.
+
+2. Importing Data
+
+- **Method:** `import_data`
+- **Function:** Loads CSV data using `QFileDialog`.
+- **Contribution by [Other Student]:** Improved file import functionality.
+
+3. Preprocessing Data and Training the Model
+
+- **Methods:** `preprocess_data`, `train_model`
+- **Functions:** One-hot encodes categorical columns, scales numerical features, and trains the model using RandomForestRegressor.
+- **Contribution by [Other Student]:** Optimized data preprocessing and model training.
+
+4. Making price Recommendations
+
+- **Method:** `predict_recommendation`
+- **Function:** Gathers user inputs, constructs input data, and predicts laptop prices.
+- **Contribution by [Other Student]:** Designed and connected user-friendly UI elements.
+
+5. Calculating Metrics
+
+- **Methods:** `calculate_mse`, `calculate_rmse`, `calculate_r_squared`, `calculate_mae`
+- **Functions:** Compute regression metrics for model evaluation.
+- **Contribution by [Other Student]:** Collaborated on accurate metric calculations.
+
+6. Data Visualization and Graphs
+
+- **Methods:** `show_data_info`, `plot_histogram`, `plot_pairplot`
+- **Functions:** Generate informative visualizations.
+- **Contribution by [Other Student]:** Designed visually appealing and informative plots.
+
+
+
 
 # Work Done
 
@@ -26,7 +78,13 @@ Om Vaghasiya
 
 1. Worked on Collection and Preprocessing of Dataset using Pandas.
 2. Modifying and Preparing Data with use of numpy arrays.
-3. Training Working  Regression Model with help of Scikit-learn.
+3. Training Working Regression Model with help of Scikit-learn.
+
+Jenil Kevadiya
+
+1. Creating a GUI Interface using PyQt6.
+2. Getting User-Inputs using different GUI elements.
+3. Integrating RandomForest Regression model to main file in order to get prediction price.