Added feature importance of Age and Estimated salary via bar graph to…

Code/Day 34 Random_Forest.md

@@ -1,85 +1,75 @@
-# Random Forests
-<p align="center">
-  <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%2033.jpg">
-</p>
-
-
-### Importing the libraries
-```python
+# Importing the libraries
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
-```
 
-### Importing the dataset
-```python
+# Importing the dataset
 dataset = pd.read_csv('Social_Network_Ads.csv')
-X = dataset.iloc[:, [2, 3]].values
-y = dataset.iloc[:, 4].values
-```
-### Splitting the dataset into the Training set and Test set
-```python
-from sklearn.cross_validation import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
-```
+X = dataset.iloc[:, [2, 3]].values  # Selecting Age and Estimated Salary columns
+y = dataset.iloc[:, 4].values  # Selecting the Purchased column
+
+# Splitting the dataset into the Training set and Test set
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
 
-### Feature Scaling
-```python
+# Feature Scaling
 from sklearn.preprocessing import StandardScaler
 sc = StandardScaler()
 X_train = sc.fit_transform(X_train)
 X_test = sc.transform(X_test)
-```
-### Fitting Random Forest to the Training set
-```python
+
+# Fitting Random Forest to the Training set
 from sklearn.ensemble import RandomForestClassifier
-classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
+classifier = RandomForestClassifier(n_estimators=10, criterion='entropy', random_state=0)
 classifier.fit(X_train, y_train)
-```
-### Predicting the Test set results
-```python
+
+# Predicting the Test set results
 y_pred = classifier.predict(X_test)
-```
-### Making the Confusion Matrix
-```python
+
+# Making the Confusion Matrix
 from sklearn.metrics import confusion_matrix
 cm = confusion_matrix(y_test, y_pred)
-```
-### Visualising the Training set results
-```python
+
+# Visualising the Training set results
 from matplotlib.colors import ListedColormap
 X_set, y_set = X_train, y_train
-X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
-                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
+X1, X2 = np.meshgrid(np.arange(start=X_set[:, 0].min() - 1, stop=X_set[:, 0].max() + 1, step=0.01),
+                     np.arange(start=X_set[:, 1].min() - 1, stop=X_set[:, 1].max() + 1, step=0.01))
 plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
-             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+             alpha=0.75, cmap=ListedColormap(('red', 'green')))
 plt.xlim(X1.min(), X1.max())
 plt.ylim(X2.min(), X2.max())
 for i, j in enumerate(np.unique(y_set)):
     plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
-                c = ListedColormap(('red', 'green'))(i), label = j)
+                c=ListedColormap(('red', 'green'))(i), label=j)
 plt.title('Random Forest Classification (Training set)')
 plt.xlabel('Age')
 plt.ylabel('Estimated Salary')
 plt.legend()
 plt.show()
-```
-### Visualising the Test set results
-```python
-from matplotlib.colors import ListedColormap
+
+# Visualising the Test set results
 X_set, y_set = X_test, y_test
-X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
-                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
+X1, X2 = np.meshgrid(np.arange(start=X_set[:, 0].min() - 1, stop=X_set[:, 0].max() + 1, step=0.01),
+                     np.arange(start=X_set[:, 1].min() - 1, stop=X_set[:, 1].max() + 1, step=0.01))
 plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
-             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+             alpha=0.75, cmap=ListedColormap(('red', 'green')))
 plt.xlim(X1.min(), X1.max())
 plt.ylim(X2.min(), X2.max())
 for i, j in enumerate(np.unique(y_set)):
     plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
-                c = ListedColormap(('red', 'green'))(i), label = j)
+                c=ListedColormap(('red', 'green'))(i), label=j)
 plt.title('Random Forest Classification (Test set)')
 plt.xlabel('Age')
 plt.ylabel('Estimated Salary')
 plt.legend()
 plt.show()
-```
+
+# Visualizing Feature Importance
+importances = classifier.feature_importances_
+features = ['Age', 'Estimated Salary']  # Naming the features
+plt.figure(figsize=(8,6))
+plt.barh(features, importances, color='skyblue')
+plt.xlabel('Importance')
+plt.title('Feature Importance in Random Forest Model')
+plt.show()