Model Training
This project, the Real_Estate_Solution.ipynb file, leverages scikit-learn to build and evaluate predictive models, focusing on real estate price prediction using techniques like Linear Regression, Decision Trees, and Random Forests.
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scikit-learn
scikit-learn (sklearn) offers simple and efficient tools for predictive data analysis. It is built on essential Python libraries, including NumPy, SciPy, and Matplotlib.
- Mounting Google Drive in Google Colab:
- Enables seamless access to files stored in Google Drive for data loading.
- Importing Required Libraries:
- Pandas: For manipulating and analyzing data in DataFrames.
- NumPy: For numerical computations.
- Matplotlib.pyplot: For data visualization.
- Enable inline plotting within the notebook to visualize results interactively.
- Importing the Data (
final.csv):- Load the dataset into a Pandas DataFrame.
- Exploring the Dataset:
- Display the first five records using the
.head()function. - Display the last five records using the
.tail()function. - View the DataFrame's dimensions using the
df.shapeattribute.
- Display the first five records using the
Linear Regression Model
1. Import the Linear Regression model fromsklearn.linear_model.
2. Separate input features into x.
3. Store the target variable in y.
Train-Test Split
1. Import thetrain_test_split function from sklearn.model_selection.
2. Split the dataset into training and testing subsets.
3. Train the Linear Regression model.
4. Display the model's coefficients (coef_) and intercept.
5. Make predictions on the training dataset.
6. Evaluate the model using the Mean Absolute Error (MAE) metric from sklearn.metrics.
Decision Tree Model
1. Import the Decision Tree Regressor fromsklearn.tree.
2. Create an instance of the Decision Tree class.
3. Train the Decision Tree model.
4. Make predictions using the test dataset.
5. Evaluate the model using MAE.
- Checking for Overfitting or Generalization:
- Make predictions on the training dataset.
- Evaluate the model's performance using MAE to determine if it overfits or generalizes well.
- Visualizing the Decision Tree:
- Retrieve the feature names.
- Plot the Decision Tree, including feature names.
- Save the visualization as
tree.png.
Random Forest Model
1. Import the Random Forest Regressor fromsklearn.ensemble.
2. Create an instance of the Random Forest model.
3. Train the Random Forest model.
4. Make predictions on the training and testing datasets.
5. Evaluate the model's performance using MAE.
Pickle for Model Serialization
1. Import thepickle module to save the trained model.
2. Save the model using pickle.dump().
3. Load the saved model using pickle.load().
4. Use the loaded model to make predictions on new data.
Through model training and evaluation, including feature engineering and error analysis, the project aims to achieve a robust real estate prediction model, integrating tools like Pickle for serialization to ensure reproducibility and deployment..