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Code/DAY 2.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#机器学习100天——第2天：简单线性回归\n",
+    "#机器学习100天——第一天：数据预处理\n",
     "##第一步：数据预处理"
    ]
   },
@@ -198,7 +198,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##第四步：可视化"
+    "##可视化"
    ]
   },
   {

Code/Day 11 K-NN.md

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+# K-Nearest Neighbors (K-NN)
+
+<p align="center">
+  <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%207.jpg">
+</p>
+
+## The DataSet | Social Network 
+
+<p align="center">
+  <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Other%20Docs/data.PNG">
+</p> 
+
+
+## Importing the libraries
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+```
+
+## Importing the dataset
+```python
+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)
+```
+## Feature Scaling
+```python
+from sklearn.preprocessing import StandardScaler
+sc = StandardScaler()
+X_train = sc.fit_transform(X_train)
+X_test = sc.transform(X_test)
+```
+## Fitting K-NN to the Training set
+```python
+from sklearn.neighbors import KNeighborsClassifier
+classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
+classifier.fit(X_train, y_train)
+```
+## Predicting the Test set results
+```python
+y_pred = classifier.predict(X_test)
+```
+
+## Making the Confusion Matrix
+```python
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)
+```