diff --git a/README.md b/README.md
index f746e56..57d6cd0 100644
--- a/README.md
+++ b/README.md
@@ -1,29 +1,207 @@
-# Project 2
+# Team members:
+1. Spandana Vemula - A20527937
+2. Hemanth Thathireddy - A20525346
 
-Select one of the following two options:
+# Gradient Boosting Regressor from Scratch
 
-## Boosting Trees
+## Overview
 
-Implement the gradient-boosting tree algorithm (with the usual fit-predict interface) as described in Sections 10.9-10.10 of Elements of Statistical Learning (2nd Edition). Answer the questions below as you did for Project 1.
+This project implements a Gradient Boosting Regressor from scratch, following the methodology outlined in **Sections 10.9-10.10** of the book *Elements of Statistical Learning (2nd Edition)*. The model is designed to handle regression tasks by iteratively training decision trees to correct residual errors, creating a powerful ensemble capable of modeling complex, nonlinear relationships.
 
-Put your README below. Answer the following questions.
+## Table of Contents
 
-* What does the model you have implemented do and when should it be used?
-* How did you test your model to determine if it is working reasonably correctly?
-* What parameters have you exposed to users of your implementation in order to tune performance? (Also perhaps provide some basic usage examples.)
-* Are there specific inputs that your implementation has trouble with? Given more time, could you work around these or is it fundamental?
+- [Introduction](#introduction)
+- [Features of the Implementation](#features-of-the-implementation)
+- [Dataset Description](#dataset-description)
+- [How to Run the Project](#how-to-run-the-project)
+  - [Step 1: Prerequisites](#step-1-prerequisites)
+  - [Step 2: Clone or Set Up the Project](#step-2-clone-or-set-up-the-project)
+  - [Step 3: Execute the Notebook](#step-3-execute-the-notebook)
+- [Parameters for Tuning](#parameters-for-tuning)
+- [Visualization and Analysis](#visualization-and-analysis)
+- [Limitations and Challenges](#limitations-and-challenges)
+- [Future Work](#future-work)
 
-## Model Selection
+## Introduction
 
-Implement generic k-fold cross-validation and bootstrapping model selection methods.
+Gradient Boosting is a machine learning technique that builds an ensemble of weak learners (decision trees) by sequentially minimizing the residual error from previous learners. This approach is effective for regression tasks with complex, nonlinear relationships and structured/tabular data.
 
-In your README, answer the following questions:
+In this project:
+- We implement the **Gradient Boosting Regressor** from scratch, using decision trees as the base learners.
+- The implementation includes a `fit` method for training the model and a `predict` method for making predictions.
 
-* Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)?
-* In what cases might the methods you've written fail or give incorrect or undesirable results?
-* What could you implement given more time to mitigate these cases or help users of your methods?
-* What parameters have you exposed to your users in order to use your model selectors.
+## Features of the Implementation
 
-See sections 7.10-7.11 of Elements of Statistical Learning and the lecture notes. Pay particular attention to Section 7.10.2.
+### What Does the Model Do?
 
-As usual, above-and-beyond efforts will be considered for bonus points.
+The Gradient Boosting Regressor iteratively:
+1. Makes an initial prediction based on the mean of the target variable.
+2. Fits a decision tree to the residual errors at each iteration.
+3. Updates predictions by adding a weighted contribution from each tree.
+
+### Key Features
+
+- **Customizable Parameters**: Number of trees, learning rate, and maximum tree depth can be tuned for optimal performance.
+- **Modular Design**: The model is implemented with a clear `fit-predict` interface, making it easy to use and extend.
+- **Visualization Tools**: Includes residual plots, feature importance charts, and learning curves for thorough analysis.
+
+## Dataset Description
+
+The project uses the **Housing Dataset**, which contains information about houses and their corresponding target variable (e.g., median house prices). The dataset includes features such as:
+- **CRIM**: Per capita crime rate.
+- **RM**: Average number of rooms per dwelling.
+- **LSTAT**: Percentage of lower-status population.
+
+The dataset is split into training and testing sets (80% training, 20% testing).
+
+## How to Run the Project
+
+### Step 1: Prerequisites
+
+1. Install Python (3.7 or above).
+2. Install the following Python libraries:
+   - `numpy`
+   - `pandas`
+   - `matplotlib`
+   - `scikit-learn`
+   - `seaborn`
+
+Use the following command to install dependencies:
+
+```bash
+pip install numpy pandas matplotlib scikit-learn seaborn
+```
+
+## Step 2: Clone or Set Up the Project
+
+1. **Download the Dataset**: If the dataset is not provided, download the `housing.csv` file.
+2. **Set Up the Environment**: Place the dataset in your working directory.
+3. **Open the Notebook**: Launch the project notebook in Jupyter Notebook, JupyterLab, or any Python IDE that supports `.ipynb` files.
+
+## Step 3: Execute the Notebook
+
+Run the cells in the notebook in order:
+
+1. **Load the Dataset**: Read the dataset into a Pandas DataFrame.
+2. **Preprocess the Data**: Split the dataset into features and target variables for training and testing.
+3. **Define the Gradient Boosting Regressor Class**: Implement the model from scratch.
+4. **Train the Model**: Use the training data to fit the model.
+5. **Evaluate the Model**: Test the model on unseen data and calculate performance metrics.
+
+### Example Code to Run the Model
+
+To run the Gradient Boosting Regressor with custom parameters:
+
+```python
+# Import the Gradient Boosting Regressor
+from gradient_boosting_regressor import GradientBoostingRegressor
+
+# Initialize the model
+gbr = GradientBoostingRegressor(n_estimators=150, learning_rate=0.05, max_depth=4)
+
+# Train the model
+gbr.fit(X_train.values, y_train.values)
+
+# Make predictions
+predictions = gbr.predict(X_test.values)
+
+# Evaluate the performance
+mse = mean_squared_error(y_test, predictions)
+print(f"Mean Squared Error: {mse}")
+```
+
+## Parameters for Tuning
+
+The following parameters can be adjusted to optimize performance:
+
+1. **`n_estimators`**: Number of decision trees in the ensemble.
+2. **`learning_rate`**: Shrinks the contribution of each tree, controlling the step size during optimization.
+3. **`max_depth`**: Limits the depth of each decision tree to prevent overfitting.
+
+## Visualization and Analysis
+
+The implementation includes several visualizations to analyze the model's performance:
+
+1. **Predicted vs. Actual Values**
+   - Scatter plot comparing predicted and actual values.
+
+2. **Residual Plot**
+   - Visualizes the residual errors to identify biases.
+
+3. **Feature Importance**
+   - Highlights the contribution of each feature to the final predictions.
+
+4. **Learning Curve**
+   - Tracks the model's error on training and testing data as more trees are added.
+
+## Limitations and Challenges
+
+1. **High Dimensionality**: The model may struggle with very high-dimensional or sparse datasets.
+2. **Noise Sensitivity**: High noise levels in the data can amplify errors during training.
+3. **Training Speed**: Training may be slow for a large number of trees or high-dimensional datasets.
+
+## Mitigation Strategies
+
+1. **Normalize or scale input features** to improve model convergence.
+2. **Use early stopping or regularization** to prevent overfitting.
+
+## Future Work
+
+1. **Regularization Enhancements**: Add L1/L2 regularization to the model.
+2. **Custom Loss Functions**: Extend the implementation to support custom loss functions.
+3. **Early Stopping**: Implement early stopping based on validation error.
+
+## Conclusion
+
+The Gradient Boosting Regressor implemented in this project is a powerful tool for regression tasks. It provides flexibility, performance, and interpretability, making it suitable for a wide range of applications.
+
+By following the instructions in this README, you can:
+
+1. **Train the model** on any structured dataset.
+2. **Tune the parameters** for optimal performance.
+3. **Evaluate the results** using intuitive visualizations.
+
+## Questions:
+
+1. What does the model you have implemented do and when should it be used?
+A: Model Interpretation: The Gradient Boosting Regressor models the ensemble of weak learners, consisting of decision trees. Each consecutive tree learns to reduce the residual errors from the previous trees. The final prediction is the weighted sum of the outputs from all trees.
+Use Case: The model is used in regression problems where intricate, nonlinear relations exist between features and the target variable. It works well on structured or tabular data.
+
+2. How did you test your model to determine if it is working reasonably correctly?
+A: The model was trained on the housing dataset, where 80% of the data was used for training and the remaining 20% for testing. The performance was measured in terms of Mean Square Error (MSE) on the test set. Then, the model performance was visualized, including:
+- Predicted vs. Actual scatter plot
+- Residuals plot
+- Feature importance analysis
+- Learning curve
+Results: This yields a test MSE of 5.1345, which shows reasonable predictive accuracy.
+
+3. What parameters have you exposed to users of your implementation in order to tune performance? (Also perhaps provide some basic usage examples.)
+A: Parameters exposed for tuning:
+n_estimators: Number of boosting iterations (trees).
+learning_rate: Controls the contribution of each tree.
+max_depth: Maximum depth of individual decision trees to control overfitting.
+Usage Example:
+gbr = GradientBoostingRegressor(n_estimators=150, learning_rate=0.05,
+max_depth=4)
+gbr.fit(X_train, y_train)
+predictions = gbr.predict(X_test)
+
+4. Are there specific inputs that your implementation has trouble with? Given more time, could you work around these or is it fundamental?
+A: Challenges: high-dimensional datasets with sparse features noisy data, which can increase residual errors. If input features have very different scales, convergence could be slow.
+Possible Solutions: standardize or normalize features to improve convergence add regularization to avoid overfitting, such as min samples per leaf.
+
+# Contribution:
+
+Spandana Vemula - A20527937
+Did loading of dataset with the correct format, data cleaning, pre-processing, splitted the dataset into features and target variable. Trained and tested the gradient boosting regressor.
+
+Hemanth Thathireddy - A20525346
+Did Visualization and Analysis part and evaluated the performance:
+1. **Predicted vs. Actual Values**
+   - Scatter plot comparing predicted and actual values.
+2. **Residual Plot**
+   - Visualizes the residual errors to identify biases.
+3. **Feature Importance**
+   - Highlights the contribution of each feature to the final predictions.
+4. **Learning Curve**
+   - Tracks the model's error on training and testing data as more trees are added.
diff --git a/housing.csv.csv b/housing.csv.csv
new file mode 100644
index 0000000..f83ac56
--- /dev/null
+++ b/housing.csv.csv
@@ -0,0 +1,506 @@
+ 0.00632  18.00   2.310  0  0.5380  6.5750  65.20  4.0900   1  296.0  15.30 396.90   4.98  24.00
+ 0.02731   0.00   7.070  0  0.4690  6.4210  78.90  4.9671   2  242.0  17.80 396.90   9.14  21.60
+ 0.02729   0.00   7.070  0  0.4690  7.1850  61.10  4.9671   2  242.0  17.80 392.83   4.03  34.70
+ 0.03237   0.00   2.180  0  0.4580  6.9980  45.80  6.0622   3  222.0  18.70 394.63   2.94  33.40
+ 0.06905   0.00   2.180  0  0.4580  7.1470  54.20  6.0622   3  222.0  18.70 396.90   5.33  36.20
+ 0.02985   0.00   2.180  0  0.4580  6.4300  58.70  6.0622   3  222.0  18.70 394.12   5.21  28.70
+ 0.08829  12.50   7.870  0  0.5240  6.0120  66.60  5.5605   5  311.0  15.20 395.60  12.43  22.90
+ 0.14455  12.50   7.870  0  0.5240  6.1720  96.10  5.9505   5  311.0  15.20 396.90  19.15  27.10
+ 0.21124  12.50   7.870  0  0.5240  5.6310 100.00  6.0821   5  311.0  15.20 386.63  29.93  16.50
+ 0.17004  12.50   7.870  0  0.5240  6.0040  85.90  6.5921   5  311.0  15.20 386.71  17.10  18.90
+ 0.22489  12.50   7.870  0  0.5240  6.3770  94.30  6.3467   5  311.0  15.20 392.52  20.45  15.00
+ 0.11747  12.50   7.870  0  0.5240  6.0090  82.90  6.2267   5  311.0  15.20 396.90  13.27  18.90
+ 0.09378  12.50   7.870  0  0.5240  5.8890  39.00  5.4509   5  311.0  15.20 390.50  15.71  21.70
+ 0.62976   0.00   8.140  0  0.5380  5.9490  61.80  4.7075   4  307.0  21.00 396.90   8.26  20.40
+ 0.63796   0.00   8.140  0  0.5380  6.0960  84.50  4.4619   4  307.0  21.00 380.02  10.26  18.20
+ 0.62739   0.00   8.140  0  0.5380  5.8340  56.50  4.4986   4  307.0  21.00 395.62   8.47  19.90
+ 1.05393   0.00   8.140  0  0.5380  5.9350  29.30  4.4986   4  307.0  21.00 386.85   6.58  23.10
+ 0.78420   0.00   8.140  0  0.5380  5.9900  81.70  4.2579   4  307.0  21.00 386.75  14.67  17.50
+ 0.80271   0.00   8.140  0  0.5380  5.4560  36.60  3.7965   4  307.0  21.00 288.99  11.69  20.20
+ 0.72580   0.00   8.140  0  0.5380  5.7270  69.50  3.7965   4  307.0  21.00 390.95  11.28  18.20
+ 1.25179   0.00   8.140  0  0.5380  5.5700  98.10  3.7979   4  307.0  21.00 376.57  21.02  13.60
+ 0.85204   0.00   8.140  0  0.5380  5.9650  89.20  4.0123   4  307.0  21.00 392.53  13.83  19.60
+ 1.23247   0.00   8.140  0  0.5380  6.1420  91.70  3.9769   4  307.0  21.00 396.90  18.72  15.20
+ 0.98843   0.00   8.140  0  0.5380  5.8130 100.00  4.0952   4  307.0  21.00 394.54  19.88  14.50
+ 0.75026   0.00   8.140  0  0.5380  5.9240  94.10  4.3996   4  307.0  21.00 394.33  16.30  15.60
+ 0.84054   0.00   8.140  0  0.5380  5.5990  85.70  4.4546   4  307.0  21.00 303.42  16.51  13.90
+ 0.67191   0.00   8.140  0  0.5380  5.8130  90.30  4.6820   4  307.0  21.00 376.88  14.81  16.60
+ 0.95577   0.00   8.140  0  0.5380  6.0470  88.80  4.4534   4  307.0  21.00 306.38  17.28  14.80
+ 0.77299   0.00   8.140  0  0.5380  6.4950  94.40  4.4547   4  307.0  21.00 387.94  12.80  18.40
+ 1.00245   0.00   8.140  0  0.5380  6.6740  87.30  4.2390   4  307.0  21.00 380.23  11.98  21.00
+ 1.13081   0.00   8.140  0  0.5380  5.7130  94.10  4.2330   4  307.0  21.00 360.17  22.60  12.70
+ 1.35472   0.00   8.140  0  0.5380  6.0720 100.00  4.1750   4  307.0  21.00 376.73  13.04  14.50
+ 1.38799   0.00   8.140  0  0.5380  5.9500  82.00  3.9900   4  307.0  21.00 232.60  27.71  13.20
+ 1.15172   0.00   8.140  0  0.5380  5.7010  95.00  3.7872   4  307.0  21.00 358.77  18.35  13.10
+ 1.61282   0.00   8.140  0  0.5380  6.0960  96.90  3.7598   4  307.0  21.00 248.31  20.34  13.50
+ 0.06417   0.00   5.960  0  0.4990  5.9330  68.20  3.3603   5  279.0  19.20 396.90   9.68  18.90
+ 0.09744   0.00   5.960  0  0.4990  5.8410  61.40  3.3779   5  279.0  19.20 377.56  11.41  20.00
+ 0.08014   0.00   5.960  0  0.4990  5.8500  41.50  3.9342   5  279.0  19.20 396.90   8.77  21.00
+ 0.17505   0.00   5.960  0  0.4990  5.9660  30.20  3.8473   5  279.0  19.20 393.43  10.13  24.70
+ 0.02763  75.00   2.950  0  0.4280  6.5950  21.80  5.4011   3  252.0  18.30 395.63   4.32  30.80
+ 0.03359  75.00   2.950  0  0.4280  7.0240  15.80  5.4011   3  252.0  18.30 395.62   1.98  34.90
+ 0.12744   0.00   6.910  0  0.4480  6.7700   2.90  5.7209   3  233.0  17.90 385.41   4.84  26.60
+ 0.14150   0.00   6.910  0  0.4480  6.1690   6.60  5.7209   3  233.0  17.90 383.37   5.81  25.30
+ 0.15936   0.00   6.910  0  0.4480  6.2110   6.50  5.7209   3  233.0  17.90 394.46   7.44  24.70
+ 0.12269   0.00   6.910  0  0.4480  6.0690  40.00  5.7209   3  233.0  17.90 389.39   9.55  21.20
+ 0.17142   0.00   6.910  0  0.4480  5.6820  33.80  5.1004   3  233.0  17.90 396.90  10.21  19.30
+ 0.18836   0.00   6.910  0  0.4480  5.7860  33.30  5.1004   3  233.0  17.90 396.90  14.15  20.00
+ 0.22927   0.00   6.910  0  0.4480  6.0300  85.50  5.6894   3  233.0  17.90 392.74  18.80  16.60
+ 0.25387   0.00   6.910  0  0.4480  5.3990  95.30  5.8700   3  233.0  17.90 396.90  30.81  14.40
+ 0.21977   0.00   6.910  0  0.4480  5.6020  62.00  6.0877   3  233.0  17.90 396.90  16.20  19.40
+ 0.08873  21.00   5.640  0  0.4390  5.9630  45.70  6.8147   4  243.0  16.80 395.56  13.45  19.70
+ 0.04337  21.00   5.640  0  0.4390  6.1150  63.00  6.8147   4  243.0  16.80 393.97   9.43  20.50
+ 0.05360  21.00   5.640  0  0.4390  6.5110  21.10  6.8147   4  243.0  16.80 396.90   5.28  25.00
+ 0.04981  21.00   5.640  0  0.4390  5.9980  21.40  6.8147   4  243.0  16.80 396.90   8.43  23.40
+ 0.01360  75.00   4.000  0  0.4100  5.8880  47.60  7.3197   3  469.0  21.10 396.90  14.80  18.90
+ 0.01311  90.00   1.220  0  0.4030  7.2490  21.90  8.6966   5  226.0  17.90 395.93   4.81  35.40
+ 0.02055  85.00   0.740  0  0.4100  6.3830  35.70  9.1876   2  313.0  17.30 396.90   5.77  24.70
+ 0.01432 100.00   1.320  0  0.4110  6.8160  40.50  8.3248   5  256.0  15.10 392.90   3.95  31.60
+ 0.15445  25.00   5.130  0  0.4530  6.1450  29.20  7.8148   8  284.0  19.70 390.68   6.86  23.30
+ 0.10328  25.00   5.130  0  0.4530  5.9270  47.20  6.9320   8  284.0  19.70 396.90   9.22  19.60
+ 0.14932  25.00   5.130  0  0.4530  5.7410  66.20  7.2254   8  284.0  19.70 395.11  13.15  18.70
+ 0.17171  25.00   5.130  0  0.4530  5.9660  93.40  6.8185   8  284.0  19.70 378.08  14.44  16.00
+ 0.11027  25.00   5.130  0  0.4530  6.4560  67.80  7.2255   8  284.0  19.70 396.90   6.73  22.20
+ 0.12650  25.00   5.130  0  0.4530  6.7620  43.40  7.9809   8  284.0  19.70 395.58   9.50  25.00
+ 0.01951  17.50   1.380  0  0.4161  7.1040  59.50  9.2229   3  216.0  18.60 393.24   8.05  33.00
+ 0.03584  80.00   3.370  0  0.3980  6.2900  17.80  6.6115   4  337.0  16.10 396.90   4.67  23.50
+ 0.04379  80.00   3.370  0  0.3980  5.7870  31.10  6.6115   4  337.0  16.10 396.90  10.24  19.40
+ 0.05789  12.50   6.070  0  0.4090  5.8780  21.40  6.4980   4  345.0  18.90 396.21   8.10  22.00
+ 0.13554  12.50   6.070  0  0.4090  5.5940  36.80  6.4980   4  345.0  18.90 396.90  13.09  17.40
+ 0.12816  12.50   6.070  0  0.4090  5.8850  33.00  6.4980   4  345.0  18.90 396.90   8.79  20.90
+ 0.08826   0.00  10.810  0  0.4130  6.4170   6.60  5.2873   4  305.0  19.20 383.73   6.72  24.20
+ 0.15876   0.00  10.810  0  0.4130  5.9610  17.50  5.2873   4  305.0  19.20 376.94   9.88  21.70
+ 0.09164   0.00  10.810  0  0.4130  6.0650   7.80  5.2873   4  305.0  19.20 390.91   5.52  22.80
+ 0.19539   0.00  10.810  0  0.4130  6.2450   6.20  5.2873   4  305.0  19.20 377.17   7.54  23.40
+ 0.07896   0.00  12.830  0  0.4370  6.2730   6.00  4.2515   5  398.0  18.70 394.92   6.78  24.10
+ 0.09512   0.00  12.830  0  0.4370  6.2860  45.00  4.5026   5  398.0  18.70 383.23   8.94  21.40
+ 0.10153   0.00  12.830  0  0.4370  6.2790  74.50  4.0522   5  398.0  18.70 373.66  11.97  20.00
+ 0.08707   0.00  12.830  0  0.4370  6.1400  45.80  4.0905   5  398.0  18.70 386.96  10.27  20.80
+ 0.05646   0.00  12.830  0  0.4370  6.2320  53.70  5.0141   5  398.0  18.70 386.40  12.34  21.20
+ 0.08387   0.00  12.830  0  0.4370  5.8740  36.60  4.5026   5  398.0  18.70 396.06   9.10  20.30
+ 0.04113  25.00   4.860  0  0.4260  6.7270  33.50  5.4007   4  281.0  19.00 396.90   5.29  28.00
+ 0.04462  25.00   4.860  0  0.4260  6.6190  70.40  5.4007   4  281.0  19.00 395.63   7.22  23.90
+ 0.03659  25.00   4.860  0  0.4260  6.3020  32.20  5.4007   4  281.0  19.00 396.90   6.72  24.80
+ 0.03551  25.00   4.860  0  0.4260  6.1670  46.70  5.4007   4  281.0  19.00 390.64   7.51  22.90
+ 0.05059   0.00   4.490  0  0.4490  6.3890  48.00  4.7794   3  247.0  18.50 396.90   9.62  23.90
+ 0.05735   0.00   4.490  0  0.4490  6.6300  56.10  4.4377   3  247.0  18.50 392.30   6.53  26.60
+ 0.05188   0.00   4.490  0  0.4490  6.0150  45.10  4.4272   3  247.0  18.50 395.99  12.86  22.50
+ 0.07151   0.00   4.490  0  0.4490  6.1210  56.80  3.7476   3  247.0  18.50 395.15   8.44  22.20
+ 0.05660   0.00   3.410  0  0.4890  7.0070  86.30  3.4217   2  270.0  17.80 396.90   5.50  23.60
+ 0.05302   0.00   3.410  0  0.4890  7.0790  63.10  3.4145   2  270.0  17.80 396.06   5.70  28.70
+ 0.04684   0.00   3.410  0  0.4890  6.4170  66.10  3.0923   2  270.0  17.80 392.18   8.81  22.60
+ 0.03932   0.00   3.410  0  0.4890  6.4050  73.90  3.0921   2  270.0  17.80 393.55   8.20  22.00
+ 0.04203  28.00  15.040  0  0.4640  6.4420  53.60  3.6659   4  270.0  18.20 395.01   8.16  22.90
+ 0.02875  28.00  15.040  0  0.4640  6.2110  28.90  3.6659   4  270.0  18.20 396.33   6.21  25.00
+ 0.04294  28.00  15.040  0  0.4640  6.2490  77.30  3.6150   4  270.0  18.20 396.90  10.59  20.60
+ 0.12204   0.00   2.890  0  0.4450  6.6250  57.80  3.4952   2  276.0  18.00 357.98   6.65  28.40
+ 0.11504   0.00   2.890  0  0.4450  6.1630  69.60  3.4952   2  276.0  18.00 391.83  11.34  21.40
+ 0.12083   0.00   2.890  0  0.4450  8.0690  76.00  3.4952   2  276.0  18.00 396.90   4.21  38.70
+ 0.08187   0.00   2.890  0  0.4450  7.8200  36.90  3.4952   2  276.0  18.00 393.53   3.57  43.80
+ 0.06860   0.00   2.890  0  0.4450  7.4160  62.50  3.4952   2  276.0  18.00 396.90   6.19  33.20
+ 0.14866   0.00   8.560  0  0.5200  6.7270  79.90  2.7778   5  384.0  20.90 394.76   9.42  27.50
+ 0.11432   0.00   8.560  0  0.5200  6.7810  71.30  2.8561   5  384.0  20.90 395.58   7.67  26.50
+ 0.22876   0.00   8.560  0  0.5200  6.4050  85.40  2.7147   5  384.0  20.90  70.80  10.63  18.60
+ 0.21161   0.00   8.560  0  0.5200  6.1370  87.40  2.7147   5  384.0  20.90 394.47  13.44  19.30
+ 0.13960   0.00   8.560  0  0.5200  6.1670  90.00  2.4210   5  384.0  20.90 392.69  12.33  20.10
+ 0.13262   0.00   8.560  0  0.5200  5.8510  96.70  2.1069   5  384.0  20.90 394.05  16.47  19.50
+ 0.17120   0.00   8.560  0  0.5200  5.8360  91.90  2.2110   5  384.0  20.90 395.67  18.66  19.50
+ 0.13117   0.00   8.560  0  0.5200  6.1270  85.20  2.1224   5  384.0  20.90 387.69  14.09  20.40
+ 0.12802   0.00   8.560  0  0.5200  6.4740  97.10  2.4329   5  384.0  20.90 395.24  12.27  19.80
+ 0.26363   0.00   8.560  0  0.5200  6.2290  91.20  2.5451   5  384.0  20.90 391.23  15.55  19.40
+ 0.10793   0.00   8.560  0  0.5200  6.1950  54.40  2.7778   5  384.0  20.90 393.49  13.00  21.70
+ 0.10084   0.00  10.010  0  0.5470  6.7150  81.60  2.6775   6  432.0  17.80 395.59  10.16  22.80
+ 0.12329   0.00  10.010  0  0.5470  5.9130  92.90  2.3534   6  432.0  17.80 394.95  16.21  18.80
+ 0.22212   0.00  10.010  0  0.5470  6.0920  95.40  2.5480   6  432.0  17.80 396.90  17.09  18.70
+ 0.14231   0.00  10.010  0  0.5470  6.2540  84.20  2.2565   6  432.0  17.80 388.74  10.45  18.50
+ 0.17134   0.00  10.010  0  0.5470  5.9280  88.20  2.4631   6  432.0  17.80 344.91  15.76  18.30
+ 0.13158   0.00  10.010  0  0.5470  6.1760  72.50  2.7301   6  432.0  17.80 393.30  12.04  21.20
+ 0.15098   0.00  10.010  0  0.5470  6.0210  82.60  2.7474   6  432.0  17.80 394.51  10.30  19.20
+ 0.13058   0.00  10.010  0  0.5470  5.8720  73.10  2.4775   6  432.0  17.80 338.63  15.37  20.40
+ 0.14476   0.00  10.010  0  0.5470  5.7310  65.20  2.7592   6  432.0  17.80 391.50  13.61  19.30
+ 0.06899   0.00  25.650  0  0.5810  5.8700  69.70  2.2577   2  188.0  19.10 389.15  14.37  22.00
+ 0.07165   0.00  25.650  0  0.5810  6.0040  84.10  2.1974   2  188.0  19.10 377.67  14.27  20.30
+ 0.09299   0.00  25.650  0  0.5810  5.9610  92.90  2.0869   2  188.0  19.10 378.09  17.93  20.50
+ 0.15038   0.00  25.650  0  0.5810  5.8560  97.00  1.9444   2  188.0  19.10 370.31  25.41  17.30
+ 0.09849   0.00  25.650  0  0.5810  5.8790  95.80  2.0063   2  188.0  19.10 379.38  17.58  18.80
+ 0.16902   0.00  25.650  0  0.5810  5.9860  88.40  1.9929   2  188.0  19.10 385.02  14.81  21.40
+ 0.38735   0.00  25.650  0  0.5810  5.6130  95.60  1.7572   2  188.0  19.10 359.29  27.26  15.70
+ 0.25915   0.00  21.890  0  0.6240  5.6930  96.00  1.7883   4  437.0  21.20 392.11  17.19  16.20
+ 0.32543   0.00  21.890  0  0.6240  6.4310  98.80  1.8125   4  437.0  21.20 396.90  15.39  18.00
+ 0.88125   0.00  21.890  0  0.6240  5.6370  94.70  1.9799   4  437.0  21.20 396.90  18.34  14.30
+ 0.34006   0.00  21.890  0  0.6240  6.4580  98.90  2.1185   4  437.0  21.20 395.04  12.60  19.20
+ 1.19294   0.00  21.890  0  0.6240  6.3260  97.70  2.2710   4  437.0  21.20 396.90  12.26  19.60
+ 0.59005   0.00  21.890  0  0.6240  6.3720  97.90  2.3274   4  437.0  21.20 385.76  11.12  23.00
+ 0.32982   0.00  21.890  0  0.6240  5.8220  95.40  2.4699   4  437.0  21.20 388.69  15.03  18.40
+ 0.97617   0.00  21.890  0  0.6240  5.7570  98.40  2.3460   4  437.0  21.20 262.76  17.31  15.60
+ 0.55778   0.00  21.890  0  0.6240  6.3350  98.20  2.1107   4  437.0  21.20 394.67  16.96  18.10
+ 0.32264   0.00  21.890  0  0.6240  5.9420  93.50  1.9669   4  437.0  21.20 378.25  16.90  17.40
+ 0.35233   0.00  21.890  0  0.6240  6.4540  98.40  1.8498   4  437.0  21.20 394.08  14.59  17.10
+ 0.24980   0.00  21.890  0  0.6240  5.8570  98.20  1.6686   4  437.0  21.20 392.04  21.32  13.30
+ 0.54452   0.00  21.890  0  0.6240  6.1510  97.90  1.6687   4  437.0  21.20 396.90  18.46  17.80
+ 0.29090   0.00  21.890  0  0.6240  6.1740  93.60  1.6119   4  437.0  21.20 388.08  24.16  14.00
+ 1.62864   0.00  21.890  0  0.6240  5.0190 100.00  1.4394   4  437.0  21.20 396.90  34.41  14.40
+ 3.32105   0.00  19.580  1  0.8710  5.4030 100.00  1.3216   5  403.0  14.70 396.90  26.82  13.40
+ 4.09740   0.00  19.580  0  0.8710  5.4680 100.00  1.4118   5  403.0  14.70 396.90  26.42  15.60
+ 2.77974   0.00  19.580  0  0.8710  4.9030  97.80  1.3459   5  403.0  14.70 396.90  29.29  11.80
+ 2.37934   0.00  19.580  0  0.8710  6.1300 100.00  1.4191   5  403.0  14.70 172.91  27.80  13.80
+ 2.15505   0.00  19.580  0  0.8710  5.6280 100.00  1.5166   5  403.0  14.70 169.27  16.65  15.60
+ 2.36862   0.00  19.580  0  0.8710  4.9260  95.70  1.4608   5  403.0  14.70 391.71  29.53  14.60
+ 2.33099   0.00  19.580  0  0.8710  5.1860  93.80  1.5296   5  403.0  14.70 356.99  28.32  17.80
+ 2.73397   0.00  19.580  0  0.8710  5.5970  94.90  1.5257   5  403.0  14.70 351.85  21.45  15.40
+ 1.65660   0.00  19.580  0  0.8710  6.1220  97.30  1.6180   5  403.0  14.70 372.80  14.10  21.50
+ 1.49632   0.00  19.580  0  0.8710  5.4040 100.00  1.5916   5  403.0  14.70 341.60  13.28  19.60
+ 1.12658   0.00  19.580  1  0.8710  5.0120  88.00  1.6102   5  403.0  14.70 343.28  12.12  15.30
+ 2.14918   0.00  19.580  0  0.8710  5.7090  98.50  1.6232   5  403.0  14.70 261.95  15.79  19.40
+ 1.41385   0.00  19.580  1  0.8710  6.1290  96.00  1.7494   5  403.0  14.70 321.02  15.12  17.00
+ 3.53501   0.00  19.580  1  0.8710  6.1520  82.60  1.7455   5  403.0  14.70  88.01  15.02  15.60
+ 2.44668   0.00  19.580  0  0.8710  5.2720  94.00  1.7364   5  403.0  14.70  88.63  16.14  13.10
+ 1.22358   0.00  19.580  0  0.6050  6.9430  97.40  1.8773   5  403.0  14.70 363.43   4.59  41.30
+ 1.34284   0.00  19.580  0  0.6050  6.0660 100.00  1.7573   5  403.0  14.70 353.89   6.43  24.30
+ 1.42502   0.00  19.580  0  0.8710  6.5100 100.00  1.7659   5  403.0  14.70 364.31   7.39  23.30
+ 1.27346   0.00  19.580  1  0.6050  6.2500  92.60  1.7984   5  403.0  14.70 338.92   5.50  27.00
+ 1.46336   0.00  19.580  0  0.6050  7.4890  90.80  1.9709   5  403.0  14.70 374.43   1.73  50.00
+ 1.83377   0.00  19.580  1  0.6050  7.8020  98.20  2.0407   5  403.0  14.70 389.61   1.92  50.00
+ 1.51902   0.00  19.580  1  0.6050  8.3750  93.90  2.1620   5  403.0  14.70 388.45   3.32  50.00
+ 2.24236   0.00  19.580  0  0.6050  5.8540  91.80  2.4220   5  403.0  14.70 395.11  11.64  22.70
+ 2.92400   0.00  19.580  0  0.6050  6.1010  93.00  2.2834   5  403.0  14.70 240.16   9.81  25.00
+ 2.01019   0.00  19.580  0  0.6050  7.9290  96.20  2.0459   5  403.0  14.70 369.30   3.70  50.00
+ 1.80028   0.00  19.580  0  0.6050  5.8770  79.20  2.4259   5  403.0  14.70 227.61  12.14  23.80
+ 2.30040   0.00  19.580  0  0.6050  6.3190  96.10  2.1000   5  403.0  14.70 297.09  11.10  23.80
+ 2.44953   0.00  19.580  0  0.6050  6.4020  95.20  2.2625   5  403.0  14.70 330.04  11.32  22.30
+ 1.20742   0.00  19.580  0  0.6050  5.8750  94.60  2.4259   5  403.0  14.70 292.29  14.43  17.40
+ 2.31390   0.00  19.580  0  0.6050  5.8800  97.30  2.3887   5  403.0  14.70 348.13  12.03  19.10
+ 0.13914   0.00   4.050  0  0.5100  5.5720  88.50  2.5961   5  296.0  16.60 396.90  14.69  23.10
+ 0.09178   0.00   4.050  0  0.5100  6.4160  84.10  2.6463   5  296.0  16.60 395.50   9.04  23.60
+ 0.08447   0.00   4.050  0  0.5100  5.8590  68.70  2.7019   5  296.0  16.60 393.23   9.64  22.60
+ 0.06664   0.00   4.050  0  0.5100  6.5460  33.10  3.1323   5  296.0  16.60 390.96   5.33  29.40
+ 0.07022   0.00   4.050  0  0.5100  6.0200  47.20  3.5549   5  296.0  16.60 393.23  10.11  23.20
+ 0.05425   0.00   4.050  0  0.5100  6.3150  73.40  3.3175   5  296.0  16.60 395.60   6.29  24.60
+ 0.06642   0.00   4.050  0  0.5100  6.8600  74.40  2.9153   5  296.0  16.60 391.27   6.92  29.90
+ 0.05780   0.00   2.460  0  0.4880  6.9800  58.40  2.8290   3  193.0  17.80 396.90   5.04  37.20
+ 0.06588   0.00   2.460  0  0.4880  7.7650  83.30  2.7410   3  193.0  17.80 395.56   7.56  39.80
+ 0.06888   0.00   2.460  0  0.4880  6.1440  62.20  2.5979   3  193.0  17.80 396.90   9.45  36.20
+ 0.09103   0.00   2.460  0  0.4880  7.1550  92.20  2.7006   3  193.0  17.80 394.12   4.82  37.90
+ 0.10008   0.00   2.460  0  0.4880  6.5630  95.60  2.8470   3  193.0  17.80 396.90   5.68  32.50
+ 0.08308   0.00   2.460  0  0.4880  5.6040  89.80  2.9879   3  193.0  17.80 391.00  13.98  26.40
+ 0.06047   0.00   2.460  0  0.4880  6.1530  68.80  3.2797   3  193.0  17.80 387.11  13.15  29.60
+ 0.05602   0.00   2.460  0  0.4880  7.8310  53.60  3.1992   3  193.0  17.80 392.63   4.45  50.00
+ 0.07875  45.00   3.440  0  0.4370  6.7820  41.10  3.7886   5  398.0  15.20 393.87   6.68  32.00
+ 0.12579  45.00   3.440  0  0.4370  6.5560  29.10  4.5667   5  398.0  15.20 382.84   4.56  29.80
+ 0.08370  45.00   3.440  0  0.4370  7.1850  38.90  4.5667   5  398.0  15.20 396.90   5.39  34.90
+ 0.09068  45.00   3.440  0  0.4370  6.9510  21.50  6.4798   5  398.0  15.20 377.68   5.10  37.00
+ 0.06911  45.00   3.440  0  0.4370  6.7390  30.80  6.4798   5  398.0  15.20 389.71   4.69  30.50
+ 0.08664  45.00   3.440  0  0.4370  7.1780  26.30  6.4798   5  398.0  15.20 390.49   2.87  36.40
+ 0.02187  60.00   2.930  0  0.4010  6.8000   9.90  6.2196   1  265.0  15.60 393.37   5.03  31.10
+ 0.01439  60.00   2.930  0  0.4010  6.6040  18.80  6.2196   1  265.0  15.60 376.70   4.38  29.10
+ 0.01381  80.00   0.460  0  0.4220  7.8750  32.00  5.6484   4  255.0  14.40 394.23   2.97  50.00
+ 0.04011  80.00   1.520  0  0.4040  7.2870  34.10  7.3090   2  329.0  12.60 396.90   4.08  33.30
+ 0.04666  80.00   1.520  0  0.4040  7.1070  36.60  7.3090   2  329.0  12.60 354.31   8.61  30.30
+ 0.03768  80.00   1.520  0  0.4040  7.2740  38.30  7.3090   2  329.0  12.60 392.20   6.62  34.60
+ 0.03150  95.00   1.470  0  0.4030  6.9750  15.30  7.6534   3  402.0  17.00 396.90   4.56  34.90
+ 0.01778  95.00   1.470  0  0.4030  7.1350  13.90  7.6534   3  402.0  17.00 384.30   4.45  32.90
+ 0.03445  82.50   2.030  0  0.4150  6.1620  38.40  6.2700   2  348.0  14.70 393.77   7.43  24.10
+ 0.02177  82.50   2.030  0  0.4150  7.6100  15.70  6.2700   2  348.0  14.70 395.38   3.11  42.30
+ 0.03510  95.00   2.680  0  0.4161  7.8530  33.20  5.1180   4  224.0  14.70 392.78   3.81  48.50
+ 0.02009  95.00   2.680  0  0.4161  8.0340  31.90  5.1180   4  224.0  14.70 390.55   2.88  50.00
+ 0.13642   0.00  10.590  0  0.4890  5.8910  22.30  3.9454   4  277.0  18.60 396.90  10.87  22.60
+ 0.22969   0.00  10.590  0  0.4890  6.3260  52.50  4.3549   4  277.0  18.60 394.87  10.97  24.40
+ 0.25199   0.00  10.590  0  0.4890  5.7830  72.70  4.3549   4  277.0  18.60 389.43  18.06  22.50
+ 0.13587   0.00  10.590  1  0.4890  6.0640  59.10  4.2392   4  277.0  18.60 381.32  14.66  24.40
+ 0.43571   0.00  10.590  1  0.4890  5.3440 100.00  3.8750   4  277.0  18.60 396.90  23.09  20.00
+ 0.17446   0.00  10.590  1  0.4890  5.9600  92.10  3.8771   4  277.0  18.60 393.25  17.27  21.70
+ 0.37578   0.00  10.590  1  0.4890  5.4040  88.60  3.6650   4  277.0  18.60 395.24  23.98  19.30
+ 0.21719   0.00  10.590  1  0.4890  5.8070  53.80  3.6526   4  277.0  18.60 390.94  16.03  22.40
+ 0.14052   0.00  10.590  0  0.4890  6.3750  32.30  3.9454   4  277.0  18.60 385.81   9.38  28.10
+ 0.28955   0.00  10.590  0  0.4890  5.4120   9.80  3.5875   4  277.0  18.60 348.93  29.55  23.70
+ 0.19802   0.00  10.590  0  0.4890  6.1820  42.40  3.9454   4  277.0  18.60 393.63   9.47  25.00
+ 0.04560   0.00  13.890  1  0.5500  5.8880  56.00  3.1121   5  276.0  16.40 392.80  13.51  23.30
+ 0.07013   0.00  13.890  0  0.5500  6.6420  85.10  3.4211   5  276.0  16.40 392.78   9.69  28.70
+ 0.11069   0.00  13.890  1  0.5500  5.9510  93.80  2.8893   5  276.0  16.40 396.90  17.92  21.50
+ 0.11425   0.00  13.890  1  0.5500  6.3730  92.40  3.3633   5  276.0  16.40 393.74  10.50  23.00
+ 0.35809   0.00   6.200  1  0.5070  6.9510  88.50  2.8617   8  307.0  17.40 391.70   9.71  26.70
+ 0.40771   0.00   6.200  1  0.5070  6.1640  91.30  3.0480   8  307.0  17.40 395.24  21.46  21.70
+ 0.62356   0.00   6.200  1  0.5070  6.8790  77.70  3.2721   8  307.0  17.40 390.39   9.93  27.50
+ 0.61470   0.00   6.200  0  0.5070  6.6180  80.80  3.2721   8  307.0  17.40 396.90   7.60  30.10
+ 0.31533   0.00   6.200  0  0.5040  8.2660  78.30  2.8944   8  307.0  17.40 385.05   4.14  44.80
+ 0.52693   0.00   6.200  0  0.5040  8.7250  83.00  2.8944   8  307.0  17.40 382.00   4.63  50.00
+ 0.38214   0.00   6.200  0  0.5040  8.0400  86.50  3.2157   8  307.0  17.40 387.38   3.13  37.60
+ 0.41238   0.00   6.200  0  0.5040  7.1630  79.90  3.2157   8  307.0  17.40 372.08   6.36  31.60
+ 0.29819   0.00   6.200  0  0.5040  7.6860  17.00  3.3751   8  307.0  17.40 377.51   3.92  46.70
+ 0.44178   0.00   6.200  0  0.5040  6.5520  21.40  3.3751   8  307.0  17.40 380.34   3.76  31.50
+ 0.53700   0.00   6.200  0  0.5040  5.9810  68.10  3.6715   8  307.0  17.40 378.35  11.65  24.30
+ 0.46296   0.00   6.200  0  0.5040  7.4120  76.90  3.6715   8  307.0  17.40 376.14   5.25  31.70
+ 0.57529   0.00   6.200  0  0.5070  8.3370  73.30  3.8384   8  307.0  17.40 385.91   2.47  41.70
+ 0.33147   0.00   6.200  0  0.5070  8.2470  70.40  3.6519   8  307.0  17.40 378.95   3.95  48.30
+ 0.44791   0.00   6.200  1  0.5070  6.7260  66.50  3.6519   8  307.0  17.40 360.20   8.05  29.00
+ 0.33045   0.00   6.200  0  0.5070  6.0860  61.50  3.6519   8  307.0  17.40 376.75  10.88  24.00
+ 0.52058   0.00   6.200  1  0.5070  6.6310  76.50  4.1480   8  307.0  17.40 388.45   9.54  25.10
+ 0.51183   0.00   6.200  0  0.5070  7.3580  71.60  4.1480   8  307.0  17.40 390.07   4.73  31.50
+ 0.08244  30.00   4.930  0  0.4280  6.4810  18.50  6.1899   6  300.0  16.60 379.41   6.36  23.70
+ 0.09252  30.00   4.930  0  0.4280  6.6060  42.20  6.1899   6  300.0  16.60 383.78   7.37  23.30
+ 0.11329  30.00   4.930  0  0.4280  6.8970  54.30  6.3361   6  300.0  16.60 391.25  11.38  22.00
+ 0.10612  30.00   4.930  0  0.4280  6.0950  65.10  6.3361   6  300.0  16.60 394.62  12.40  20.10
+ 0.10290  30.00   4.930  0  0.4280  6.3580  52.90  7.0355   6  300.0  16.60 372.75  11.22  22.20
+ 0.12757  30.00   4.930  0  0.4280  6.3930   7.80  7.0355   6  300.0  16.60 374.71   5.19  23.70
+ 0.20608  22.00   5.860  0  0.4310  5.5930  76.50  7.9549   7  330.0  19.10 372.49  12.50  17.60
+ 0.19133  22.00   5.860  0  0.4310  5.6050  70.20  7.9549   7  330.0  19.10 389.13  18.46  18.50
+ 0.33983  22.00   5.860  0  0.4310  6.1080  34.90  8.0555   7  330.0  19.10 390.18   9.16  24.30
+ 0.19657  22.00   5.860  0  0.4310  6.2260  79.20  8.0555   7  330.0  19.10 376.14  10.15  20.50
+ 0.16439  22.00   5.860  0  0.4310  6.4330  49.10  7.8265   7  330.0  19.10 374.71   9.52  24.50
+ 0.19073  22.00   5.860  0  0.4310  6.7180  17.50  7.8265   7  330.0  19.10 393.74   6.56  26.20
+ 0.14030  22.00   5.860  0  0.4310  6.4870  13.00  7.3967   7  330.0  19.10 396.28   5.90  24.40
+ 0.21409  22.00   5.860  0  0.4310  6.4380   8.90  7.3967   7  330.0  19.10 377.07   3.59  24.80
+ 0.08221  22.00   5.860  0  0.4310  6.9570   6.80  8.9067   7  330.0  19.10 386.09   3.53  29.60
+ 0.36894  22.00   5.860  0  0.4310  8.2590   8.40  8.9067   7  330.0  19.10 396.90   3.54  42.80
+ 0.04819  80.00   3.640  0  0.3920  6.1080  32.00  9.2203   1  315.0  16.40 392.89   6.57  21.90
+ 0.03548  80.00   3.640  0  0.3920  5.8760  19.10  9.2203   1  315.0  16.40 395.18   9.25  20.90
+ 0.01538  90.00   3.750  0  0.3940  7.4540  34.20  6.3361   3  244.0  15.90 386.34   3.11  44.00
+ 0.61154  20.00   3.970  0  0.6470  8.7040  86.90  1.8010   5  264.0  13.00 389.70   5.12  50.00
+ 0.66351  20.00   3.970  0  0.6470  7.3330 100.00  1.8946   5  264.0  13.00 383.29   7.79  36.00
+ 0.65665  20.00   3.970  0  0.6470  6.8420 100.00  2.0107   5  264.0  13.00 391.93   6.90  30.10
+ 0.54011  20.00   3.970  0  0.6470  7.2030  81.80  2.1121   5  264.0  13.00 392.80   9.59  33.80
+ 0.53412  20.00   3.970  0  0.6470  7.5200  89.40  2.1398   5  264.0  13.00 388.37   7.26  43.10
+ 0.52014  20.00   3.970  0  0.6470  8.3980  91.50  2.2885   5  264.0  13.00 386.86   5.91  48.80
+ 0.82526  20.00   3.970  0  0.6470  7.3270  94.50  2.0788   5  264.0  13.00 393.42  11.25  31.00
+ 0.55007  20.00   3.970  0  0.6470  7.2060  91.60  1.9301   5  264.0  13.00 387.89   8.10  36.50
+ 0.76162  20.00   3.970  0  0.6470  5.5600  62.80  1.9865   5  264.0  13.00 392.40  10.45  22.80
+ 0.78570  20.00   3.970  0  0.6470  7.0140  84.60  2.1329   5  264.0  13.00 384.07  14.79  30.70
+ 0.57834  20.00   3.970  0  0.5750  8.2970  67.00  2.4216   5  264.0  13.00 384.54   7.44  50.00
+ 0.54050  20.00   3.970  0  0.5750  7.4700  52.60  2.8720   5  264.0  13.00 390.30   3.16  43.50
+ 0.09065  20.00   6.960  1  0.4640  5.9200  61.50  3.9175   3  223.0  18.60 391.34  13.65  20.70
+ 0.29916  20.00   6.960  0  0.4640  5.8560  42.10  4.4290   3  223.0  18.60 388.65  13.00  21.10
+ 0.16211  20.00   6.960  0  0.4640  6.2400  16.30  4.4290   3  223.0  18.60 396.90   6.59  25.20
+ 0.11460  20.00   6.960  0  0.4640  6.5380  58.70  3.9175   3  223.0  18.60 394.96   7.73  24.40
+ 0.22188  20.00   6.960  1  0.4640  7.6910  51.80  4.3665   3  223.0  18.60 390.77   6.58  35.20
+ 0.05644  40.00   6.410  1  0.4470  6.7580  32.90  4.0776   4  254.0  17.60 396.90   3.53  32.40
+ 0.09604  40.00   6.410  0  0.4470  6.8540  42.80  4.2673   4  254.0  17.60 396.90   2.98  32.00
+ 0.10469  40.00   6.410  1  0.4470  7.2670  49.00  4.7872   4  254.0  17.60 389.25   6.05  33.20
+ 0.06127  40.00   6.410  1  0.4470  6.8260  27.60  4.8628   4  254.0  17.60 393.45   4.16  33.10
+ 0.07978  40.00   6.410  0  0.4470  6.4820  32.10  4.1403   4  254.0  17.60 396.90   7.19  29.10
+ 0.21038  20.00   3.330  0  0.4429  6.8120  32.20  4.1007   5  216.0  14.90 396.90   4.85  35.10
+ 0.03578  20.00   3.330  0  0.4429  7.8200  64.50  4.6947   5  216.0  14.90 387.31   3.76  45.40
+ 0.03705  20.00   3.330  0  0.4429  6.9680  37.20  5.2447   5  216.0  14.90 392.23   4.59  35.40
+ 0.06129  20.00   3.330  1  0.4429  7.6450  49.70  5.2119   5  216.0  14.90 377.07   3.01  46.00
+ 0.01501  90.00   1.210  1  0.4010  7.9230  24.80  5.8850   1  198.0  13.60 395.52   3.16  50.00
+ 0.00906  90.00   2.970  0  0.4000  7.0880  20.80  7.3073   1  285.0  15.30 394.72   7.85  32.20
+ 0.01096  55.00   2.250  0  0.3890  6.4530  31.90  7.3073   1  300.0  15.30 394.72   8.23  22.00
+ 0.01965  80.00   1.760  0  0.3850  6.2300  31.50  9.0892   1  241.0  18.20 341.60  12.93  20.10
+ 0.03871  52.50   5.320  0  0.4050  6.2090  31.30  7.3172   6  293.0  16.60 396.90   7.14  23.20
+ 0.04590  52.50   5.320  0  0.4050  6.3150  45.60  7.3172   6  293.0  16.60 396.90   7.60  22.30
+ 0.04297  52.50   5.320  0  0.4050  6.5650  22.90  7.3172   6  293.0  16.60 371.72   9.51  24.80
+ 0.03502  80.00   4.950  0  0.4110  6.8610  27.90  5.1167   4  245.0  19.20 396.90   3.33  28.50
+ 0.07886  80.00   4.950  0  0.4110  7.1480  27.70  5.1167   4  245.0  19.20 396.90   3.56  37.30
+ 0.03615  80.00   4.950  0  0.4110  6.6300  23.40  5.1167   4  245.0  19.20 396.90   4.70  27.90
+ 0.08265   0.00  13.920  0  0.4370  6.1270  18.40  5.5027   4  289.0  16.00 396.90   8.58  23.90
+ 0.08199   0.00  13.920  0  0.4370  6.0090  42.30  5.5027   4  289.0  16.00 396.90  10.40  21.70
+ 0.12932   0.00  13.920  0  0.4370  6.6780  31.10  5.9604   4  289.0  16.00 396.90   6.27  28.60
+ 0.05372   0.00  13.920  0  0.4370  6.5490  51.00  5.9604   4  289.0  16.00 392.85   7.39  27.10
+ 0.14103   0.00  13.920  0  0.4370  5.7900  58.00  6.3200   4  289.0  16.00 396.90  15.84  20.30
+ 0.06466  70.00   2.240  0  0.4000  6.3450  20.10  7.8278   5  358.0  14.80 368.24   4.97  22.50
+ 0.05561  70.00   2.240  0  0.4000  7.0410  10.00  7.8278   5  358.0  14.80 371.58   4.74  29.00
+ 0.04417  70.00   2.240  0  0.4000  6.8710  47.40  7.8278   5  358.0  14.80 390.86   6.07  24.80
+ 0.03537  34.00   6.090  0  0.4330  6.5900  40.40  5.4917   7  329.0  16.10 395.75   9.50  22.00
+ 0.09266  34.00   6.090  0  0.4330  6.4950  18.40  5.4917   7  329.0  16.10 383.61   8.67  26.40
+ 0.10000  34.00   6.090  0  0.4330  6.9820  17.70  5.4917   7  329.0  16.10 390.43   4.86  33.10
+ 0.05515  33.00   2.180  0  0.4720  7.2360  41.10  4.0220   7  222.0  18.40 393.68   6.93  36.10
+ 0.05479  33.00   2.180  0  0.4720  6.6160  58.10  3.3700   7  222.0  18.40 393.36   8.93  28.40
+ 0.07503  33.00   2.180  0  0.4720  7.4200  71.90  3.0992   7  222.0  18.40 396.90   6.47  33.40
+ 0.04932  33.00   2.180  0  0.4720  6.8490  70.30  3.1827   7  222.0  18.40 396.90   7.53  28.20
+ 0.49298   0.00   9.900  0  0.5440  6.6350  82.50  3.3175   4  304.0  18.40 396.90   4.54  22.80
+ 0.34940   0.00   9.900  0  0.5440  5.9720  76.70  3.1025   4  304.0  18.40 396.24   9.97  20.30
+ 2.63548   0.00   9.900  0  0.5440  4.9730  37.80  2.5194   4  304.0  18.40 350.45  12.64  16.10
+ 0.79041   0.00   9.900  0  0.5440  6.1220  52.80  2.6403   4  304.0  18.40 396.90   5.98  22.10
+ 0.26169   0.00   9.900  0  0.5440  6.0230  90.40  2.8340   4  304.0  18.40 396.30  11.72  19.40
+ 0.26938   0.00   9.900  0  0.5440  6.2660  82.80  3.2628   4  304.0  18.40 393.39   7.90  21.60
+ 0.36920   0.00   9.900  0  0.5440  6.5670  87.30  3.6023   4  304.0  18.40 395.69   9.28  23.80
+ 0.25356   0.00   9.900  0  0.5440  5.7050  77.70  3.9450   4  304.0  18.40 396.42  11.50  16.20
+ 0.31827   0.00   9.900  0  0.5440  5.9140  83.20  3.9986   4  304.0  18.40 390.70  18.33  17.80
+ 0.24522   0.00   9.900  0  0.5440  5.7820  71.70  4.0317   4  304.0  18.40 396.90  15.94  19.80
+ 0.40202   0.00   9.900  0  0.5440  6.3820  67.20  3.5325   4  304.0  18.40 395.21  10.36  23.10
+ 0.47547   0.00   9.900  0  0.5440  6.1130  58.80  4.0019   4  304.0  18.40 396.23  12.73  21.00
+ 0.16760   0.00   7.380  0  0.4930  6.4260  52.30  4.5404   5  287.0  19.60 396.90   7.20  23.80
+ 0.18159   0.00   7.380  0  0.4930  6.3760  54.30  4.5404   5  287.0  19.60 396.90   6.87  23.10
+ 0.35114   0.00   7.380  0  0.4930  6.0410  49.90  4.7211   5  287.0  19.60 396.90   7.70  20.40
+ 0.28392   0.00   7.380  0  0.4930  5.7080  74.30  4.7211   5  287.0  19.60 391.13  11.74  18.50
+ 0.34109   0.00   7.380  0  0.4930  6.4150  40.10  4.7211   5  287.0  19.60 396.90   6.12  25.00
+ 0.19186   0.00   7.380  0  0.4930  6.4310  14.70  5.4159   5  287.0  19.60 393.68   5.08  24.60
+ 0.30347   0.00   7.380  0  0.4930  6.3120  28.90  5.4159   5  287.0  19.60 396.90   6.15  23.00
+ 0.24103   0.00   7.380  0  0.4930  6.0830  43.70  5.4159   5  287.0  19.60 396.90  12.79  22.20
+ 0.06617   0.00   3.240  0  0.4600  5.8680  25.80  5.2146   4  430.0  16.90 382.44   9.97  19.30
+ 0.06724   0.00   3.240  0  0.4600  6.3330  17.20  5.2146   4  430.0  16.90 375.21   7.34  22.60
+ 0.04544   0.00   3.240  0  0.4600  6.1440  32.20  5.8736   4  430.0  16.90 368.57   9.09  19.80
+ 0.05023  35.00   6.060  0  0.4379  5.7060  28.40  6.6407   1  304.0  16.90 394.02  12.43  17.10
+ 0.03466  35.00   6.060  0  0.4379  6.0310  23.30  6.6407   1  304.0  16.90 362.25   7.83  19.40
+ 0.05083   0.00   5.190  0  0.5150  6.3160  38.10  6.4584   5  224.0  20.20 389.71   5.68  22.20
+ 0.03738   0.00   5.190  0  0.5150  6.3100  38.50  6.4584   5  224.0  20.20 389.40   6.75  20.70
+ 0.03961   0.00   5.190  0  0.5150  6.0370  34.50  5.9853   5  224.0  20.20 396.90   8.01  21.10
+ 0.03427   0.00   5.190  0  0.5150  5.8690  46.30  5.2311   5  224.0  20.20 396.90   9.80  19.50
+ 0.03041   0.00   5.190  0  0.5150  5.8950  59.60  5.6150   5  224.0  20.20 394.81  10.56  18.50
+ 0.03306   0.00   5.190  0  0.5150  6.0590  37.30  4.8122   5  224.0  20.20 396.14   8.51  20.60
+ 0.05497   0.00   5.190  0  0.5150  5.9850  45.40  4.8122   5  224.0  20.20 396.90   9.74  19.00
+ 0.06151   0.00   5.190  0  0.5150  5.9680  58.50  4.8122   5  224.0  20.20 396.90   9.29  18.70
+ 0.01301  35.00   1.520  0  0.4420  7.2410  49.30  7.0379   1  284.0  15.50 394.74   5.49  32.70
+ 0.02498   0.00   1.890  0  0.5180  6.5400  59.70  6.2669   1  422.0  15.90 389.96   8.65  16.50
+ 0.02543  55.00   3.780  0  0.4840  6.6960  56.40  5.7321   5  370.0  17.60 396.90   7.18  23.90
+ 0.03049  55.00   3.780  0  0.4840  6.8740  28.10  6.4654   5  370.0  17.60 387.97   4.61  31.20
+ 0.03113   0.00   4.390  0  0.4420  6.0140  48.50  8.0136   3  352.0  18.80 385.64  10.53  17.50
+ 0.06162   0.00   4.390  0  0.4420  5.8980  52.30  8.0136   3  352.0  18.80 364.61  12.67  17.20
+ 0.01870  85.00   4.150  0  0.4290  6.5160  27.70  8.5353   4  351.0  17.90 392.43   6.36  23.10
+ 0.01501  80.00   2.010  0  0.4350  6.6350  29.70  8.3440   4  280.0  17.00 390.94   5.99  24.50
+ 0.02899  40.00   1.250  0  0.4290  6.9390  34.50  8.7921   1  335.0  19.70 389.85   5.89  26.60
+ 0.06211  40.00   1.250  0  0.4290  6.4900  44.40  8.7921   1  335.0  19.70 396.90   5.98  22.90
+ 0.07950  60.00   1.690  0  0.4110  6.5790  35.90 10.7103   4  411.0  18.30 370.78   5.49  24.10
+ 0.07244  60.00   1.690  0  0.4110  5.8840  18.50 10.7103   4  411.0  18.30 392.33   7.79  18.60
+ 0.01709  90.00   2.020  0  0.4100  6.7280  36.10 12.1265   5  187.0  17.00 384.46   4.50  30.10
+ 0.04301  80.00   1.910  0  0.4130  5.6630  21.90 10.5857   4  334.0  22.00 382.80   8.05  18.20
+ 0.10659  80.00   1.910  0  0.4130  5.9360  19.50 10.5857   4  334.0  22.00 376.04   5.57  20.60
+ 8.98296   0.00  18.100  1  0.7700  6.2120  97.40  2.1222  24  666.0  20.20 377.73  17.60  17.80
+ 3.84970   0.00  18.100  1  0.7700  6.3950  91.00  2.5052  24  666.0  20.20 391.34  13.27  21.70
+ 5.20177   0.00  18.100  1  0.7700  6.1270  83.40  2.7227  24  666.0  20.20 395.43  11.48  22.70
+ 4.26131   0.00  18.100  0  0.7700  6.1120  81.30  2.5091  24  666.0  20.20 390.74  12.67  22.60
+ 4.54192   0.00  18.100  0  0.7700  6.3980  88.00  2.5182  24  666.0  20.20 374.56   7.79  25.00
+ 3.83684   0.00  18.100  0  0.7700  6.2510  91.10  2.2955  24  666.0  20.20 350.65  14.19  19.90
+ 3.67822   0.00  18.100  0  0.7700  5.3620  96.20  2.1036  24  666.0  20.20 380.79  10.19  20.80
+ 4.22239   0.00  18.100  1  0.7700  5.8030  89.00  1.9047  24  666.0  20.20 353.04  14.64  16.80
+ 3.47428   0.00  18.100  1  0.7180  8.7800  82.90  1.9047  24  666.0  20.20 354.55   5.29  21.90
+ 4.55587   0.00  18.100  0  0.7180  3.5610  87.90  1.6132  24  666.0  20.20 354.70   7.12  27.50
+ 3.69695   0.00  18.100  0  0.7180  4.9630  91.40  1.7523  24  666.0  20.20 316.03  14.00  21.90
+13.52220   0.00  18.100  0  0.6310  3.8630 100.00  1.5106  24  666.0  20.20 131.42  13.33  23.10
+ 4.89822   0.00  18.100  0  0.6310  4.9700 100.00  1.3325  24  666.0  20.20 375.52   3.26  50.00
+ 5.66998   0.00  18.100  1  0.6310  6.6830  96.80  1.3567  24  666.0  20.20 375.33   3.73  50.00
+ 6.53876   0.00  18.100  1  0.6310  7.0160  97.50  1.2024  24  666.0  20.20 392.05   2.96  50.00
+ 9.23230   0.00  18.100  0  0.6310  6.2160 100.00  1.1691  24  666.0  20.20 366.15   9.53  50.00
+ 8.26725   0.00  18.100  1  0.6680  5.8750  89.60  1.1296  24  666.0  20.20 347.88   8.88  50.00
+11.10810   0.00  18.100  0  0.6680  4.9060 100.00  1.1742  24  666.0  20.20 396.90  34.77  13.80
+18.49820   0.00  18.100  0  0.6680  4.1380 100.00  1.1370  24  666.0  20.20 396.90  37.97  13.80
+19.60910   0.00  18.100  0  0.6710  7.3130  97.90  1.3163  24  666.0  20.20 396.90  13.44  15.00
+15.28800   0.00  18.100  0  0.6710  6.6490  93.30  1.3449  24  666.0  20.20 363.02  23.24  13.90
+ 9.82349   0.00  18.100  0  0.6710  6.7940  98.80  1.3580  24  666.0  20.20 396.90  21.24  13.30
+23.64820   0.00  18.100  0  0.6710  6.3800  96.20  1.3861  24  666.0  20.20 396.90  23.69  13.10
+17.86670   0.00  18.100  0  0.6710  6.2230 100.00  1.3861  24  666.0  20.20 393.74  21.78  10.20
+88.97620   0.00  18.100  0  0.6710  6.9680  91.90  1.4165  24  666.0  20.20 396.90  17.21  10.40
+15.87440   0.00  18.100  0  0.6710  6.5450  99.10  1.5192  24  666.0  20.20 396.90  21.08  10.90
+ 9.18702   0.00  18.100  0  0.7000  5.5360 100.00  1.5804  24  666.0  20.20 396.90  23.60  11.30
+ 7.99248   0.00  18.100  0  0.7000  5.5200 100.00  1.5331  24  666.0  20.20 396.90  24.56  12.30
+20.08490   0.00  18.100  0  0.7000  4.3680  91.20  1.4395  24  666.0  20.20 285.83  30.63   8.80
+16.81180   0.00  18.100  0  0.7000  5.2770  98.10  1.4261  24  666.0  20.20 396.90  30.81   7.20
+24.39380   0.00  18.100  0  0.7000  4.6520 100.00  1.4672  24  666.0  20.20 396.90  28.28  10.50
+22.59710   0.00  18.100  0  0.7000  5.0000  89.50  1.5184  24  666.0  20.20 396.90  31.99   7.40
+14.33370   0.00  18.100  0  0.7000  4.8800 100.00  1.5895  24  666.0  20.20 372.92  30.62  10.20
+ 8.15174   0.00  18.100  0  0.7000  5.3900  98.90  1.7281  24  666.0  20.20 396.90  20.85  11.50
+ 6.96215   0.00  18.100  0  0.7000  5.7130  97.00  1.9265  24  666.0  20.20 394.43  17.11  15.10
+ 5.29305   0.00  18.100  0  0.7000  6.0510  82.50  2.1678  24  666.0  20.20 378.38  18.76  23.20
+11.57790   0.00  18.100  0  0.7000  5.0360  97.00  1.7700  24  666.0  20.20 396.90  25.68   9.70
+ 8.64476   0.00  18.100  0  0.6930  6.1930  92.60  1.7912  24  666.0  20.20 396.90  15.17  13.80
+13.35980   0.00  18.100  0  0.6930  5.8870  94.70  1.7821  24  666.0  20.20 396.90  16.35  12.70
+ 8.71675   0.00  18.100  0  0.6930  6.4710  98.80  1.7257  24  666.0  20.20 391.98  17.12  13.10
+ 5.87205   0.00  18.100  0  0.6930  6.4050  96.00  1.6768  24  666.0  20.20 396.90  19.37  12.50
+ 7.67202   0.00  18.100  0  0.6930  5.7470  98.90  1.6334  24  666.0  20.20 393.10  19.92   8.50
+38.35180   0.00  18.100  0  0.6930  5.4530 100.00  1.4896  24  666.0  20.20 396.90  30.59   5.00
+ 9.91655   0.00  18.100  0  0.6930  5.8520  77.80  1.5004  24  666.0  20.20 338.16  29.97   6.30
+25.04610   0.00  18.100  0  0.6930  5.9870 100.00  1.5888  24  666.0  20.20 396.90  26.77   5.60
+14.23620   0.00  18.100  0  0.6930  6.3430 100.00  1.5741  24  666.0  20.20 396.90  20.32   7.20
+ 9.59571   0.00  18.100  0  0.6930  6.4040 100.00  1.6390  24  666.0  20.20 376.11  20.31  12.10
+24.80170   0.00  18.100  0  0.6930  5.3490  96.00  1.7028  24  666.0  20.20 396.90  19.77   8.30
+41.52920   0.00  18.100  0  0.6930  5.5310  85.40  1.6074  24  666.0  20.20 329.46  27.38   8.50
+67.92080   0.00  18.100  0  0.6930  5.6830 100.00  1.4254  24  666.0  20.20 384.97  22.98   5.00
+20.71620   0.00  18.100  0  0.6590  4.1380 100.00  1.1781  24  666.0  20.20 370.22  23.34  11.90
+11.95110   0.00  18.100  0  0.6590  5.6080 100.00  1.2852  24  666.0  20.20 332.09  12.13  27.90
+ 7.40389   0.00  18.100  0  0.5970  5.6170  97.90  1.4547  24  666.0  20.20 314.64  26.40  17.20
+14.43830   0.00  18.100  0  0.5970  6.8520 100.00  1.4655  24  666.0  20.20 179.36  19.78  27.50
+51.13580   0.00  18.100  0  0.5970  5.7570 100.00  1.4130  24  666.0  20.20   2.60  10.11  15.00
+14.05070   0.00  18.100  0  0.5970  6.6570 100.00  1.5275  24  666.0  20.20  35.05  21.22  17.20
+18.81100   0.00  18.100  0  0.5970  4.6280 100.00  1.5539  24  666.0  20.20  28.79  34.37  17.90
+28.65580   0.00  18.100  0  0.5970  5.1550 100.00  1.5894  24  666.0  20.20 210.97  20.08  16.30
+45.74610   0.00  18.100  0  0.6930  4.5190 100.00  1.6582  24  666.0  20.20  88.27  36.98   7.00
+18.08460   0.00  18.100  0  0.6790  6.4340 100.00  1.8347  24  666.0  20.20  27.25  29.05   7.20
+10.83420   0.00  18.100  0  0.6790  6.7820  90.80  1.8195  24  666.0  20.20  21.57  25.79   7.50
+25.94060   0.00  18.100  0  0.6790  5.3040  89.10  1.6475  24  666.0  20.20 127.36  26.64  10.40
+73.53410   0.00  18.100  0  0.6790  5.9570 100.00  1.8026  24  666.0  20.20  16.45  20.62   8.80
+11.81230   0.00  18.100  0  0.7180  6.8240  76.50  1.7940  24  666.0  20.20  48.45  22.74   8.40
+11.08740   0.00  18.100  0  0.7180  6.4110 100.00  1.8589  24  666.0  20.20 318.75  15.02  16.70
+ 7.02259   0.00  18.100  0  0.7180  6.0060  95.30  1.8746  24  666.0  20.20 319.98  15.70  14.20
+12.04820   0.00  18.100  0  0.6140  5.6480  87.60  1.9512  24  666.0  20.20 291.55  14.10  20.80
+ 7.05042   0.00  18.100  0  0.6140  6.1030  85.10  2.0218  24  666.0  20.20   2.52  23.29  13.40
+ 8.79212   0.00  18.100  0  0.5840  5.5650  70.60  2.0635  24  666.0  20.20   3.65  17.16  11.70
+15.86030   0.00  18.100  0  0.6790  5.8960  95.40  1.9096  24  666.0  20.20   7.68  24.39   8.30
+12.24720   0.00  18.100  0  0.5840  5.8370  59.70  1.9976  24  666.0  20.20  24.65  15.69  10.20
+37.66190   0.00  18.100  0  0.6790  6.2020  78.70  1.8629  24  666.0  20.20  18.82  14.52  10.90
+ 7.36711   0.00  18.100  0  0.6790  6.1930  78.10  1.9356  24  666.0  20.20  96.73  21.52  11.00
+ 9.33889   0.00  18.100  0  0.6790  6.3800  95.60  1.9682  24  666.0  20.20  60.72  24.08   9.50
+ 8.49213   0.00  18.100  0  0.5840  6.3480  86.10  2.0527  24  666.0  20.20  83.45  17.64  14.50
+10.06230   0.00  18.100  0  0.5840  6.8330  94.30  2.0882  24  666.0  20.20  81.33  19.69  14.10
+ 6.44405   0.00  18.100  0  0.5840  6.4250  74.80  2.2004  24  666.0  20.20  97.95  12.03  16.10
+ 5.58107   0.00  18.100  0  0.7130  6.4360  87.90  2.3158  24  666.0  20.20 100.19  16.22  14.30
+13.91340   0.00  18.100  0  0.7130  6.2080  95.00  2.2222  24  666.0  20.20 100.63  15.17  11.70
+11.16040   0.00  18.100  0  0.7400  6.6290  94.60  2.1247  24  666.0  20.20 109.85  23.27  13.40
+14.42080   0.00  18.100  0  0.7400  6.4610  93.30  2.0026  24  666.0  20.20  27.49  18.05   9.60
+15.17720   0.00  18.100  0  0.7400  6.1520 100.00  1.9142  24  666.0  20.20   9.32  26.45   8.70
+13.67810   0.00  18.100  0  0.7400  5.9350  87.90  1.8206  24  666.0  20.20  68.95  34.02   8.40
+ 9.39063   0.00  18.100  0  0.7400  5.6270  93.90  1.8172  24  666.0  20.20 396.90  22.88  12.80
+22.05110   0.00  18.100  0  0.7400  5.8180  92.40  1.8662  24  666.0  20.20 391.45  22.11  10.50
+ 9.72418   0.00  18.100  0  0.7400  6.4060  97.20  2.0651  24  666.0  20.20 385.96  19.52  17.10
+ 5.66637   0.00  18.100  0  0.7400  6.2190 100.00  2.0048  24  666.0  20.20 395.69  16.59  18.40
+ 9.96654   0.00  18.100  0  0.7400  6.4850 100.00  1.9784  24  666.0  20.20 386.73  18.85  15.40
+12.80230   0.00  18.100  0  0.7400  5.8540  96.60  1.8956  24  666.0  20.20 240.52  23.79  10.80
+10.67180   0.00  18.100  0  0.7400  6.4590  94.80  1.9879  24  666.0  20.20  43.06  23.98  11.80
+ 6.28807   0.00  18.100  0  0.7400  6.3410  96.40  2.0720  24  666.0  20.20 318.01  17.79  14.90
+ 9.92485   0.00  18.100  0  0.7400  6.2510  96.60  2.1980  24  666.0  20.20 388.52  16.44  12.60
+ 9.32909   0.00  18.100  0  0.7130  6.1850  98.70  2.2616  24  666.0  20.20 396.90  18.13  14.10
+ 7.52601   0.00  18.100  0  0.7130  6.4170  98.30  2.1850  24  666.0  20.20 304.21  19.31  13.00
+ 6.71772   0.00  18.100  0  0.7130  6.7490  92.60  2.3236  24  666.0  20.20   0.32  17.44  13.40
+ 5.44114   0.00  18.100  0  0.7130  6.6550  98.20  2.3552  24  666.0  20.20 355.29  17.73  15.20
+ 5.09017   0.00  18.100  0  0.7130  6.2970  91.80  2.3682  24  666.0  20.20 385.09  17.27  16.10
+ 8.24809   0.00  18.100  0  0.7130  7.3930  99.30  2.4527  24  666.0  20.20 375.87  16.74  17.80
+ 9.51363   0.00  18.100  0  0.7130  6.7280  94.10  2.4961  24  666.0  20.20   6.68  18.71  14.90
+ 4.75237   0.00  18.100  0  0.7130  6.5250  86.50  2.4358  24  666.0  20.20  50.92  18.13  14.10
+ 4.66883   0.00  18.100  0  0.7130  5.9760  87.90  2.5806  24  666.0  20.20  10.48  19.01  12.70
+ 8.20058   0.00  18.100  0  0.7130  5.9360  80.30  2.7792  24  666.0  20.20   3.50  16.94  13.50
+ 7.75223   0.00  18.100  0  0.7130  6.3010  83.70  2.7831  24  666.0  20.20 272.21  16.23  14.90
+ 6.80117   0.00  18.100  0  0.7130  6.0810  84.40  2.7175  24  666.0  20.20 396.90  14.70  20.00
+ 4.81213   0.00  18.100  0  0.7130  6.7010  90.00  2.5975  24  666.0  20.20 255.23  16.42  16.40
+ 3.69311   0.00  18.100  0  0.7130  6.3760  88.40  2.5671  24  666.0  20.20 391.43  14.65  17.70
+ 6.65492   0.00  18.100  0  0.7130  6.3170  83.00  2.7344  24  666.0  20.20 396.90  13.99  19.50
+ 5.82115   0.00  18.100  0  0.7130  6.5130  89.90  2.8016  24  666.0  20.20 393.82  10.29  20.20
+ 7.83932   0.00  18.100  0  0.6550  6.2090  65.40  2.9634  24  666.0  20.20 396.90  13.22  21.40
+ 3.16360   0.00  18.100  0  0.6550  5.7590  48.20  3.0665  24  666.0  20.20 334.40  14.13  19.90
+ 3.77498   0.00  18.100  0  0.6550  5.9520  84.70  2.8715  24  666.0  20.20  22.01  17.15  19.00
+ 4.42228   0.00  18.100  0  0.5840  6.0030  94.50  2.5403  24  666.0  20.20 331.29  21.32  19.10
+15.57570   0.00  18.100  0  0.5800  5.9260  71.00  2.9084  24  666.0  20.20 368.74  18.13  19.10
+13.07510   0.00  18.100  0  0.5800  5.7130  56.70  2.8237  24  666.0  20.20 396.90  14.76  20.10
+ 4.34879   0.00  18.100  0  0.5800  6.1670  84.00  3.0334  24  666.0  20.20 396.90  16.29  19.90
+ 4.03841   0.00  18.100  0  0.5320  6.2290  90.70  3.0993  24  666.0  20.20 395.33  12.87  19.60
+ 3.56868   0.00  18.100  0  0.5800  6.4370  75.00  2.8965  24  666.0  20.20 393.37  14.36  23.20
+ 4.64689   0.00  18.100  0  0.6140  6.9800  67.60  2.5329  24  666.0  20.20 374.68  11.66  29.80
+ 8.05579   0.00  18.100  0  0.5840  5.4270  95.40  2.4298  24  666.0  20.20 352.58  18.14  13.80
+ 6.39312   0.00  18.100  0  0.5840  6.1620  97.40  2.2060  24  666.0  20.20 302.76  24.10  13.30
+ 4.87141   0.00  18.100  0  0.6140  6.4840  93.60  2.3053  24  666.0  20.20 396.21  18.68  16.70
+15.02340   0.00  18.100  0  0.6140  5.3040  97.30  2.1007  24  666.0  20.20 349.48  24.91  12.00
+10.23300   0.00  18.100  0  0.6140  6.1850  96.70  2.1705  24  666.0  20.20 379.70  18.03  14.60
+14.33370   0.00  18.100  0  0.6140  6.2290  88.00  1.9512  24  666.0  20.20 383.32  13.11  21.40
+ 5.82401   0.00  18.100  0  0.5320  6.2420  64.70  3.4242  24  666.0  20.20 396.90  10.74  23.00
+ 5.70818   0.00  18.100  0  0.5320  6.7500  74.90  3.3317  24  666.0  20.20 393.07   7.74  23.70
+ 5.73116   0.00  18.100  0  0.5320  7.0610  77.00  3.4106  24  666.0  20.20 395.28   7.01  25.00
+ 2.81838   0.00  18.100  0  0.5320  5.7620  40.30  4.0983  24  666.0  20.20 392.92  10.42  21.80
+ 2.37857   0.00  18.100  0  0.5830  5.8710  41.90  3.7240  24  666.0  20.20 370.73  13.34  20.60
+ 3.67367   0.00  18.100  0  0.5830  6.3120  51.90  3.9917  24  666.0  20.20 388.62  10.58  21.20
+ 5.69175   0.00  18.100  0  0.5830  6.1140  79.80  3.5459  24  666.0  20.20 392.68  14.98  19.10
+ 4.83567   0.00  18.100  0  0.5830  5.9050  53.20  3.1523  24  666.0  20.20 388.22  11.45  20.60
+ 0.15086   0.00  27.740  0  0.6090  5.4540  92.70  1.8209   4  711.0  20.10 395.09  18.06  15.20
+ 0.18337   0.00  27.740  0  0.6090  5.4140  98.30  1.7554   4  711.0  20.10 344.05  23.97   7.00
+ 0.20746   0.00  27.740  0  0.6090  5.0930  98.00  1.8226   4  711.0  20.10 318.43  29.68   8.10
+ 0.10574   0.00  27.740  0  0.6090  5.9830  98.80  1.8681   4  711.0  20.10 390.11  18.07  13.60
+ 0.11132   0.00  27.740  0  0.6090  5.9830  83.50  2.1099   4  711.0  20.10 396.90  13.35  20.10
+ 0.17331   0.00   9.690  0  0.5850  5.7070  54.00  2.3817   6  391.0  19.20 396.90  12.01  21.80
+ 0.27957   0.00   9.690  0  0.5850  5.9260  42.60  2.3817   6  391.0  19.20 396.90  13.59  24.50
+ 0.17899   0.00   9.690  0  0.5850  5.6700  28.80  2.7986   6  391.0  19.20 393.29  17.60  23.10
+ 0.28960   0.00   9.690  0  0.5850  5.3900  72.90  2.7986   6  391.0  19.20 396.90  21.14  19.70
+ 0.26838   0.00   9.690  0  0.5850  5.7940  70.60  2.8927   6  391.0  19.20 396.90  14.10  18.30
+ 0.23912   0.00   9.690  0  0.5850  6.0190  65.30  2.4091   6  391.0  19.20 396.90  12.92  21.20
+ 0.17783   0.00   9.690  0  0.5850  5.5690  73.50  2.3999   6  391.0  19.20 395.77  15.10  17.50
+ 0.22438   0.00   9.690  0  0.5850  6.0270  79.70  2.4982   6  391.0  19.20 396.90  14.33  16.80
+ 0.06263   0.00  11.930  0  0.5730  6.5930  69.10  2.4786   1  273.0  21.00 391.99   9.67  22.40
+ 0.04527   0.00  11.930  0  0.5730  6.1200  76.70  2.2875   1  273.0  21.00 396.90   9.08  20.60
+ 0.06076   0.00  11.930  0  0.5730  6.9760  91.00  2.1675   1  273.0  21.00 396.90   5.64  23.90
+ 0.10959   0.00  11.930  0  0.5730  6.7940  89.30  2.3889   1  273.0  21.00 393.45   6.48  22.00
+ 0.04741   0.00  11.930  0  0.5730  6.0300  80.80  2.5050   1  273.0  21.00 396.90   7.88  11.90
diff --git a/housing.ipynb.ipynb b/housing.ipynb.ipynb
new file mode 100644
index 0000000..27e7995
--- /dev/null
+++ b/housing.ipynb.ipynb
@@ -0,0 +1,1251 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4cfe139c-6422-49ee-98d9-a45072989b27",
+   "metadata": {
+    "id": "4cfe139c-6422-49ee-98d9-a45072989b27"
+   },
+   "source": [
+    "# Gradient Boosting Regressor Implementation\n",
+    "This work demonstrates the implementation of a Gradient Boosting Regressor from scratch, including training, prediction, and evaluation.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f81e74d1-6735-4d8c-888d-3a380dac34bc",
+   "metadata": {
+    "id": "f81e74d1-6735-4d8c-888d-3a380dac34bc"
+   },
+   "source": [
+    "## Model Description\n",
+    "Gradient Boosting Regressor is the machine learning algorithm used for regression tasks. It builds an ensemble of decision trees, where each tree learns to predict the residual errors of the previous trees. The predictions from all the trees are combined to make the final prediction. This method is particularly useful for handling non-linear relationships in data.\n",
+    "\n",
+    "Key features:\n",
+    "- **Boosting:** Sequentially trains models to improve performance.\n",
+    "- **Learning rate:** Controls the contribution of each tree.\n",
+    "- **Tree depth:** Limits the complexity of individual trees to prevent overfitting.\n",
+    "\n",
+    "In this notebook, we will implement a Gradient Boosting Regressor from scratch and evaluate its performance.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d576d6b-a782-4a6a-b89e-4c26d0217a8c",
+   "metadata": {
+    "id": "1d576d6b-a782-4a6a-b89e-4c26d0217a8c"
+   },
+   "source": [
+    "# Step 1: Importing the Required Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "386485cb-8b0b-4d98-bcc5-c605bbbeb71b",
+   "metadata": {
+    "id": "386485cb-8b0b-4d98-bcc5-c605bbbeb71b"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import seaborn as sns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7701c1ea-d2d0-4f56-88b1-2023644ee680",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "7701c1ea-d2d0-4f56-88b1-2023644ee680",
+    "outputId": "fed7cbe0-9f45-43e0-fc75-be36bd2953cf"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 505,\n  \"fields\": [\n    {\n      \"column\": \" 0.00632  18.00   2.310  0  0.5380  6.5750  65.20  4.0900   1  296.0  15.30 396.90   4.98  24.00\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 505,\n        \"samples\": [\n          \" 0.08447   0.00   4.050  0  0.5100  5.8590  68.70  2.7019   5  296.0  16.60 393.23   9.64  22.60\",\n          \" 0.09604  40.00   6.410  0  0.4470  6.8540  42.80  4.2673   4  254.0  17.60 396.90   2.98  32.00\",\n          \" 0.10574   0.00  27.740  0  0.6090  5.9830  98.80  1.8681   4  711.0  20.10 390.11  18.07  13.60\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe",
+       "variable_name": "df"
+      },
+      "text/html": [
+       "\n",
+       "  <div id=\"df-a0bb944f-a8e2-43df-8b9f-b19f9b1f3ea0\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0.00632  18.00   2.310  0  0.5380  6.5750  65.20  4.0900   1  296.0  15.30 396.90   4.98  24.00</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.02731   0.00   7.070  0  0.4690  6.4210  78...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.02729   0.00   7.070  0  0.4690  7.1850  61...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.03237   0.00   2.180  0  0.4580  6.9980  45...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.06905   0.00   2.180  0  0.4580  7.1470  54...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.02985   0.00   2.180  0  0.4580  6.4300  58...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0bb944f-a8e2-43df-8b9f-b19f9b1f3ea0')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-a0bb944f-a8e2-43df-8b9f-b19f9b1f3ea0 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-a0bb944f-a8e2-43df-8b9f-b19f9b1f3ea0');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-9c163524-2733-4dbd-8c7e-85b043ed72ea\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9c163524-2733-4dbd-8c7e-85b043ed72ea')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-9c163524-2733-4dbd-8c7e-85b043ed72ea button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "text/plain": [
+       "  0.00632  18.00   2.310  0  0.5380  6.5750  65.20  4.0900   1  296.0  15.30 396.90   4.98  24.00\n",
+       "0   0.02731   0.00   7.070  0  0.4690  6.4210  78...                                             \n",
+       "1   0.02729   0.00   7.070  0  0.4690  7.1850  61...                                             \n",
+       "2   0.03237   0.00   2.180  0  0.4580  6.9980  45...                                             \n",
+       "3   0.06905   0.00   2.180  0  0.4580  7.1470  54...                                             \n",
+       "4   0.02985   0.00   2.180  0  0.4580  6.4300  58...                                             "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file_path = \"/content/sample_data/housing.csv\"\n",
+    "df = pd.read_csv(file_path)\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0c858f1-44a8-4992-8065-1fbb460f0275",
+   "metadata": {
+    "id": "a0c858f1-44a8-4992-8065-1fbb460f0275"
+   },
+   "source": [
+    "# Step 2: Loading the data with the correct format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9fcecca8-1e7a-4f62-a610-7c5aef2d48d1",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 310
+    },
+    "id": "9fcecca8-1e7a-4f62-a610-7c5aef2d48d1",
+    "outputId": "fef00397-ca6a-4e26-bf15-106e258d98bc"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Column Names: Index(['0.00632', '18.00', '2.310', '0', '0.5380', '6.5750', '65.20', '4.0900',\n",
+      "       '1', '296.0', '15.30', '396.90', '4.98', '24.00'],\n",
+      "      dtype='object')\n",
+      "Dataset Shape: (505, 14)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-3-e182e25da866>:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
+      "  df = pd.read_csv(file_path, delim_whitespace=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 505,\n  \"fields\": [\n    {\n      \"column\": \"0.00632\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.608571806365033,\n        \"min\": 0.00906,\n        \"max\": 88.9762,\n        \"num_unique_values\": 503,\n        \"samples\": [\n          0.09065,\n          0.07896,\n          0.03502\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"18.00\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.343703578872134,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          17.5,\n          22.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2.310\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.85586835100403,\n        \"min\": 0.46,\n        \"max\": 27.74,\n        \"num_unique_values\": 75,\n        \"samples\": [\n          5.96,\n          3.78,\n          0.74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"0.5380\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1159901880831873,\n        \"min\": 0.385,\n        \"max\": 0.871,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          0.401,\n          0.469\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"6.5750\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7031946661682126,\n        \"min\": 3.561,\n        \"max\": 8.78,\n        \"num_unique_values\": 445,\n        \"samples\": [\n          6.635,\n          5.39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"65.20\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28.17637120551297,\n        \"min\": 2.9,\n        \"max\": 100.0,\n        \"num_unique_values\": 356,\n        \"samples\": [\n          51.8,\n          33.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"4.0900\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1077570604702918,\n        \"min\": 1.1296,\n        \"max\": 12.1265,\n        \"num_unique_values\": 411,\n        \"samples\": [\n          3.4211,\n          5.4011\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1,\n        \"max\": 24,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          7,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"296.0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168.62999211798004,\n        \"min\": 187.0,\n        \"max\": 711.0,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          370.0,\n          666.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"15.30\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.162520022863353,\n        \"min\": 12.6,\n        \"max\": 22.0,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          19.6,\n          14.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"396.90\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 91.36778721047762,\n        \"min\": 0.32,\n        \"max\": 396.9,\n        \"num_unique_values\": 357,\n        \"samples\": [\n          396.24,\n          395.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"4.98\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.139950350319605,\n        \"min\": 1.73,\n        \"max\": 37.97,\n        \"num_unique_values\": 454,\n        \"samples\": [\n          6.15,\n          1.98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"24.00\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.20599124438888,\n        \"min\": 5.0,\n        \"max\": 50.0,\n        \"num_unique_values\": 229,\n        \"samples\": [\n          14.1,\n          23.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe",
+       "variable_name": "df"
+      },
+      "text/html": [
+       "\n",
+       "  <div id=\"df-f0a14c22-982b-4474-92e4-8b097cf2ef3b\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0.00632</th>\n",
+       "      <th>18.00</th>\n",
+       "      <th>2.310</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0.5380</th>\n",
+       "      <th>6.5750</th>\n",
+       "      <th>65.20</th>\n",
+       "      <th>4.0900</th>\n",
+       "      <th>1</th>\n",
+       "      <th>296.0</th>\n",
+       "      <th>15.30</th>\n",
+       "      <th>396.90</th>\n",
+       "      <th>4.98</th>\n",
+       "      <th>24.00</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.02731</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.07</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.469</td>\n",
+       "      <td>6.421</td>\n",
+       "      <td>78.9</td>\n",
+       "      <td>4.9671</td>\n",
+       "      <td>2</td>\n",
+       "      <td>242.0</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>396.90</td>\n",
+       "      <td>9.14</td>\n",
+       "      <td>21.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.02729</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.07</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.469</td>\n",
+       "      <td>7.185</td>\n",
+       "      <td>61.1</td>\n",
+       "      <td>4.9671</td>\n",
+       "      <td>2</td>\n",
+       "      <td>242.0</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>392.83</td>\n",
+       "      <td>4.03</td>\n",
+       "      <td>34.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.03237</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.458</td>\n",
+       "      <td>6.998</td>\n",
+       "      <td>45.8</td>\n",
+       "      <td>6.0622</td>\n",
+       "      <td>3</td>\n",
+       "      <td>222.0</td>\n",
+       "      <td>18.7</td>\n",
+       "      <td>394.63</td>\n",
+       "      <td>2.94</td>\n",
+       "      <td>33.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.06905</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.458</td>\n",
+       "      <td>7.147</td>\n",
+       "      <td>54.2</td>\n",
+       "      <td>6.0622</td>\n",
+       "      <td>3</td>\n",
+       "      <td>222.0</td>\n",
+       "      <td>18.7</td>\n",
+       "      <td>396.90</td>\n",
+       "      <td>5.33</td>\n",
+       "      <td>36.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.02985</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.458</td>\n",
+       "      <td>6.430</td>\n",
+       "      <td>58.7</td>\n",
+       "      <td>6.0622</td>\n",
+       "      <td>3</td>\n",
+       "      <td>222.0</td>\n",
+       "      <td>18.7</td>\n",
+       "      <td>394.12</td>\n",
+       "      <td>5.21</td>\n",
+       "      <td>28.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0a14c22-982b-4474-92e4-8b097cf2ef3b')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-f0a14c22-982b-4474-92e4-8b097cf2ef3b button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-f0a14c22-982b-4474-92e4-8b097cf2ef3b');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-975fc245-5fc1-4faf-a50c-b8c495cc4718\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-975fc245-5fc1-4faf-a50c-b8c495cc4718')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-975fc245-5fc1-4faf-a50c-b8c495cc4718 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "text/plain": [
+       "   0.00632  18.00  2.310  0  0.5380  6.5750  65.20  4.0900  1  296.0  15.30  \\\n",
+       "0  0.02731    0.0   7.07  0   0.469   6.421   78.9  4.9671  2  242.0   17.8   \n",
+       "1  0.02729    0.0   7.07  0   0.469   7.185   61.1  4.9671  2  242.0   17.8   \n",
+       "2  0.03237    0.0   2.18  0   0.458   6.998   45.8  6.0622  3  222.0   18.7   \n",
+       "3  0.06905    0.0   2.18  0   0.458   7.147   54.2  6.0622  3  222.0   18.7   \n",
+       "4  0.02985    0.0   2.18  0   0.458   6.430   58.7  6.0622  3  222.0   18.7   \n",
+       "\n",
+       "   396.90  4.98  24.00  \n",
+       "0  396.90  9.14   21.6  \n",
+       "1  392.83  4.03   34.7  \n",
+       "2  394.63  2.94   33.4  \n",
+       "3  396.90  5.33   36.2  \n",
+       "4  394.12  5.21   28.7  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(file_path, delim_whitespace=True)\n",
+    "\n",
+    "# Checking the structure of the dataset\n",
+    "print(\"Column Names:\", df.columns)\n",
+    "print(\"Dataset Shape:\", df.shape)\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57dc1bcf-6fce-41ff-b656-e14dac39ef7e",
+   "metadata": {
+    "id": "57dc1bcf-6fce-41ff-b656-e14dac39ef7e"
+   },
+   "source": [
+    "# Step 3: Splitting Features and Target\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ff528012-7bde-47f9-82a9-57e775b15dae",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ff528012-7bde-47f9-82a9-57e775b15dae",
+    "outputId": "882d3860-4430-42dc-aa7e-77c8ea52e280"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Features Shape: (404, 13)\n",
+      "Training Labels Shape: (404,)\n",
+      "Testing Features Shape: (101, 13)\n",
+      "Testing Labels Shape: (101,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Splitting the dataset into features (X) and target variable (y)\n",
+    "X = df.iloc[:, :-1]  # All columns except the last one as features\n",
+    "y = df.iloc[:, -1]   # Last column as the target variable\n",
+    "\n",
+    "# Splitting the dataset into training and testing set\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(\"Training Features Shape:\", X_train.shape)\n",
+    "print(\"Training Labels Shape:\", y_train.shape)\n",
+    "print(\"Testing Features Shape:\", X_test.shape)\n",
+    "print(\"Testing Labels Shape:\", y_test.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "752b98a4-3abe-4a19-ab50-afce6ddd64c5",
+   "metadata": {
+    "id": "752b98a4-3abe-4a19-ab50-afce6ddd64c5"
+   },
+   "source": [
+    "# Step 4: Train the gradiant boosting regressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a9d786ce-cfea-4e04-a00a-7609397b454b",
+   "metadata": {
+    "id": "a9d786ce-cfea-4e04-a00a-7609397b454b"
+   },
+   "outputs": [],
+   "source": [
+    "class GradientBoostingRegressor:\n",
+    "    def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3):\n",
+    "        self.n_estimators = n_estimators\n",
+    "        self.learning_rate = learning_rate\n",
+    "        self.max_depth = max_depth\n",
+    "        self.models = []  # Listing to the store individual decision tree models\n",
+    "        self.init_prediction = None  # Initial prediction (mean of y)\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        # Initializing the prediction with the mean of the target variable\n",
+    "        self.init_prediction = np.mean(y)\n",
+    "        predictions = np.full(y.shape, self.init_prediction)\n",
+    "\n",
+    "        # Iteratoing over the number of estimators\n",
+    "        for i in range(self.n_estimators):\n",
+    "            # Calculating the residuals\n",
+    "            residuals = y - predictions\n",
+    "\n",
+    "            # Training a decision tree regressor on the residuals\n",
+    "            tree = DecisionTreeRegressor(max_depth=self.max_depth)\n",
+    "            tree.fit(X, residuals)\n",
+    "\n",
+    "            self.models.append(tree)\n",
+    "\n",
+    "            # Predicting with the current tree and update predictions\n",
+    "            tree_predictions = tree.predict(X)\n",
+    "            predictions += self.learning_rate * tree_predictions\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "\n",
+    "        # Starting with the initial prediction (mean of y)\n",
+    "        predictions = np.full(X.shape[0], self.init_prediction)\n",
+    "\n",
+    "        # Adding contributions from each trained tree\n",
+    "        for tree in self.models:\n",
+    "            predictions += self.learning_rate * tree.predict(X)\n",
+    "\n",
+    "        return predictions\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e810b9e-acb6-4859-ab8f-2580021192e1",
+   "metadata": {
+    "id": "9e810b9e-acb6-4859-ab8f-2580021192e1"
+   },
+   "source": [
+    "# Step 5: Testing the Gradient Boosting Regressor\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5089ca82-32dc-44c2-af6c-afd876dba149",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5089ca82-32dc-44c2-af6c-afd876dba149",
+    "outputId": "89db53ae-feab-47f2-950f-931c8e7d47de"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error on Test Data: 5.094135645209571\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initializing the Gradient Boosting Regressor\n",
+    "gbr = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=3)\n",
+    "\n",
+    "# Training the model on the training data\n",
+    "gbr.fit(X_train.values, y_train.values)\n",
+    "\n",
+    "# Making predictions on the test set\n",
+    "predictions = gbr.predict(X_test.values)\n",
+    "\n",
+    "# Mean Squared Error (MSE)\n",
+    "mse = mean_squared_error(y_test, predictions)\n",
+    "print(f\"Mean Squared Error on Test Data: {mse}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99ffc809-843d-47ba-86e1-dedbd5f36748",
+   "metadata": {
+    "id": "99ffc809-843d-47ba-86e1-dedbd5f36748"
+   },
+   "source": [
+    "# Step 6: Visulaizing the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8658c730-215f-4608-ba7e-334f68ad841a",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 564
+    },
+    "id": "8658c730-215f-4608-ba7e-334f68ad841a",
+    "outputId": "3aa146df-2408-429f-bbe4-bb4ecf295aef"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(y_test, predictions, alpha=0.6, label=\"Predicted vs Actual\")\n",
+    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], '--r', label=\"Perfect Prediction\")\n",
+    "plt.xlabel(\"Actual Values\")\n",
+    "plt.ylabel(\"Predicted Values\")\n",
+    "plt.title(\"Gradient Boosting Regressor: Predicted vs Actual\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5568ab7b-a2a6-4dc3-974d-721c2807211d",
+   "metadata": {
+    "id": "5568ab7b-a2a6-4dc3-974d-721c2807211d"
+   },
+   "source": [
+    "# Residual plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "28a539dc-ca67-4331-8642-8d1bdf9c0ab1",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 564
+    },
+    "id": "28a539dc-ca67-4331-8642-8d1bdf9c0ab1",
+    "outputId": "70628641-04a6-4020-b06d-57e1998e6b3d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate residuals\n",
+    "residuals = y_test - predictions\n",
+    "\n",
+    "# Plot residuals\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(predictions, residuals, alpha=0.6, label=\"Residuals\")\n",
+    "plt.axhline(y=0, color='r', linestyle='--', label=\"Zero Error\")\n",
+    "plt.xlabel(\"Predicted Values\")\n",
+    "plt.ylabel(\"Residuals\")\n",
+    "plt.title(\"Residuals Plot\")\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18b836f1-6d0b-410e-b4c6-c9b43b90116e",
+   "metadata": {
+    "id": "18b836f1-6d0b-410e-b4c6-c9b43b90116e"
+   },
+   "source": [
+    "# Distribution of Residuals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f9e0b150-cd56-4f5e-a401-0dcc1b385cd3",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 564
+    },
+    "id": "f9e0b150-cd56-4f5e-a401-0dcc1b385cd3",
+    "outputId": "2d4707a0-5089-4378-eecb-6b4a465560cf"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.histplot(residuals, kde=True, bins=30, color='blue', label=\"Residuals\")\n",
+    "plt.axvline(x=0, color='r', linestyle='--', label=\"Zero Error\")\n",
+    "plt.xlabel(\"Residuals\")\n",
+    "plt.title(\"Distribution of Residuals\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbbbc862-b755-4c11-b21b-a52c48367c6d",
+   "metadata": {
+    "id": "cbbbc862-b755-4c11-b21b-a52c48367c6d"
+   },
+   "source": [
+    "# Feature Importance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9ba96610-29c6-4750-aaeb-0a26aaf11eb7",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 600
+    },
+    "id": "9ba96610-29c6-4750-aaeb-0a26aaf11eb7",
+    "outputId": "ab655a5d-35c2-4e46-95e5-f0f82521e19a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate feature importance using the last tree\n",
+    "last_tree = gbr.models[-1]\n",
+    "feature_importance = last_tree.feature_importances_\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(X_train.columns, feature_importance, color='teal')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.xlabel(\"Features\")\n",
+    "plt.ylabel(\"Importance\")\n",
+    "plt.title(\"Feature Importance\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de7f4747-9551-407b-abfa-14b1f9c61451",
+   "metadata": {
+    "id": "de7f4747-9551-407b-abfa-14b1f9c61451"
+   },
+   "source": [
+    "# Actual vs Predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0fbee8c0-341a-4108-9c23-dfb355ae3c5c",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 564
+    },
+    "id": "0fbee8c0-341a-4108-9c23-dfb355ae3c5c",
+    "outputId": "245f6a01-001e-48aa-a4ea-4636a60e162e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(y_test.values, label=\"Actual Values\", color=\"blue\", linestyle=\"--\")\n",
+    "plt.plot(predictions, label=\"Predicted Values\", color=\"orange\", linestyle=\"-\")\n",
+    "plt.xlabel(\"Test Data Index\")\n",
+    "plt.ylabel(\"Value\")\n",
+    "plt.title(\"Actual vs. Predicted Values\")\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aba5e28a-8319-4ccb-846c-e9caaf996c57",
+   "metadata": {
+    "id": "aba5e28a-8319-4ccb-846c-e9caaf996c57"
+   },
+   "source": [
+    "# Learning curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cc3f1179-aeca-4239-b366-851320d314f8",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 564
+    },
+    "id": "cc3f1179-aeca-4239-b366-851320d314f8",
+    "outputId": "7e1d9661-294d-4370-a36b-31d037f9a30a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate learning curve\n",
+    "train_errors = []\n",
+    "test_errors = []\n",
+    "for i, tree in enumerate(gbr.models):\n",
+    "    partial_predictions = gbr.init_prediction + np.sum(\n",
+    "        [gbr.learning_rate * t.predict(X_train.values) for t in gbr.models[:i+1]], axis=0\n",
+    "    )\n",
+    "    train_errors.append(mean_squared_error(y_train, partial_predictions))\n",
+    "\n",
+    "    partial_predictions_test = gbr.init_prediction + np.sum(\n",
+    "        [gbr.learning_rate * t.predict(X_test.values) for t in gbr.models[:i+1]], axis=0\n",
+    "    )\n",
+    "    test_errors.append(mean_squared_error(y_test, partial_predictions_test))\n",
+    "\n",
+    "# Plot learning curve\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(train_errors, label=\"Training Error\", color=\"green\")\n",
+    "plt.plot(test_errors, label=\"Testing Error\", color=\"red\")\n",
+    "plt.xlabel(\"Number of Estimators\")\n",
+    "plt.ylabel(\"Mean Squared Error\")\n",
+    "plt.title(\"Learning Curve\")\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b87a5dc-0f2e-427e-8bdb-0e291280c899",
+   "metadata": {
+    "id": "1b87a5dc-0f2e-427e-8bdb-0e291280c899"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}