???? KNIME Machine Learning Pipeline: House Price Predictor! ????

# Building My First KNIME Machine Learning Pipeline: A Journey in Data Science

I am thrilled to share the experience of creating my first Machine Learning pipeline using KNIME! This project was a deep dive into predicting house prices with a linear regression model, and it offered invaluable lessons in data preprocessing, model training, and evaluation. Here’s a detailed look at the process and key takeaways:

## Key Steps in the Pipeline:

1. Data Import and Exploration:

- Utilized CSV Reader nodes to bring in the training and testing datasets.

- Conducted an exploratory data analysis using the Statistics View to understand feature distributions and central tendencies. This step was crucial in gaining initial insights into the data.

2. Data Manipulation:

- Employed Python Script nodes for handling missing values and ensuring that all numeric columns were correctly formatted. This step ensured data integrity and prepared the dataset for reliable modeling.

3. Normalization:

- Applied normalization to numeric features to align variable scales, which is essential for optimizing linear regression performance. This step improved the model’s accuracy and robustness.

4. Feature Selection:

- Used Linear Correlation and Correlation Filter nodes to identify and select the most relevant features. Feature selection is a critical step that reduces noise, prevents overfitting, and enhances model performance.

5. Model Training and Evaluation:

- Trained a Linear Regression model using the Linear Regression Learner node.

- Evaluated the model with the Numeric Scorer node, obtaining detailed performance metrics that guided further refinement.

6. Visualization:

- Leveraged Box Plot, Conditional Box Plot, and Scatter Plot Matrix nodes for visualizing relationships and distributions. Visualizations were instrumental in uncovering data patterns and potential issues.

## Model Evaluation: Linear Regression Results

Here are the performance metrics of the linear regression model:

1. R2 (Coefficient of Determination): 0.9192

- Explains 91.92% of the variance in the target variable, indicating high model accuracy.

2. MAE (Mean Absolute Error): 14915.7070

- Shows that, on average, the model's predictions deviate by 14,915.7070 units from the actual values, highlighting good predictive precision.

3. MSE (Mean Squared Error): 5.0958e8

- Represents the average squared difference between predicted and actual values, with lower values signifying better fit.

4. RMSE (Root Mean Squared Error): 22573.8465

- Measures the standard deviation of prediction errors, indicating precise predictions.

5. P-Value: 4.7395e-10

- A very low p-value, demonstrating the statistical significance of the model's coefficients.

6. Adjusted R2: 0.9192

- Confirms that the inclusion of predictors is justified and that the model does not overfit.

## Summary

- R2 and Adjusted R2 values demonstrate high explanatory power.

- MAE and RMSE values show the model’s predictions are close to actual values.

- The p-value signifies that the model's coefficients are highly significant.

These results suggest that the linear regression model performs exceptionally well, with high explanatory power and accurate predictions.

## Key Learnings:

- Data Preprocessing: Ensuring clean, well-prepared data is crucial for model accuracy.

- Feature Selection: Selecting relevant features significantly impacts model performance.

- Model Evaluation: Understanding performance metrics is vital for assessing model effectiveness.

This journey has not only deepened my understanding of machine learning and data science but also highlighted the powerful capabilities of KNIME in building and refining predictive models. I am eager to continue exploring and applying these skills in future projects.

Special Thanks ??

A heartfelt thank you to Kunaal Naik for the invaluable guidance throughout this project. Your insights and support were crucial in making this learning experience so rewarding!

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