Linear Regression Tutorial Fixed for Questions

Author: mGalarnyk

.DS_Store

@@ -341,8 +341,9 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python2"
   },
@@ -356,7 +357,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,

PySpark_Basics/PySpark_Part1_Word_Count_Removing_Punctuation_Pride_Prejudice.ipynb

@@ -4,13 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Linear Regression Python Tutorial by Michael Galarnyk <p></p><p></p>\n",
-    "youtube video on how to install anaconda on mac osx: <p></p>\n",
-    "<a href = \"https://www.youtube.com/watch?v=B6d5LrA8bNE\" style=\"color: rgb(0,0,255)\"><u> https://www.youtube.com/watch?v=B6d5LrA8bNE </u> </a><p></p><p></p>\n",
-    "\n",
-    "youtube video explaining linear regression using python (this notebook): <p></p><p></p>\n",
-    "<a href = \"https://www.youtube.com/watch?v=dSYJVbj4Eew\" style=\"color: rgb(0,0,255)\"><u>https://www.youtube.com/watch?v=dSYJVbj4Eew </u> </a><p></p><p></p>\n",
-    "\n"
+    "Linear Regression Python Tutorial <p></p><p></p>"
    ]
   },
   {
@@ -89,7 +83,7 @@
    ],
    "source": [
     "raw_data = pd.read_csv(\"linear.csv\") #any dataset will work. You can get the data from my github\n",
-    "# https://github.com/mGalarnyk/Linear_Regression\n",
+    "# https://github.com/mGalarnyk/Python_Tutorials/blob/master/Python_Basics/Linear_Regression/linear.csv\n",
     "raw_data.head(3)"
    ]
   },
@@ -236,8 +230,9 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python2"
   },
@@ -251,7 +246,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,

Python_Basics/.DS_Store

@@ -0,0 +1,30 @@
+import numpy as np
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+import matplotlib.pyplot as plt
+
+raw_data = pd.read_csv("linear.csv")
+
+# Removes rows with NaN in them
+
+filtered_data = raw_data[~np.isnan(raw_data["y"])] 
+
+
+
+npMatrix = np.matrix(filtered_data)
+X, Y = npMatrix[:,0], npMatrix[:,1]
+mdl = LinearRegression().fit(X,Y) # either this or the next line
+#mdl = LinearRegression().fit(filtered_data[['x']],filtered_data.y)
+m = mdl.coef_[0]
+b = mdl.intercept_
+
+# show alternate way to get equation of the line
+print "formula: y = {0}x + {1}".format(m, b) # following slope intercept form 
+
+# 
+# show how to plot using non python notebooks
+plt.scatter(X,Y, color='blue')
+plt.plot([0,100],[b,m*100+b],'r')
+plt.title('Linear Regression Example', fontsize = 20)
+plt.xlabel('X', fontsize = 15)
+plt.ylabel('Y', fontsize = 15)

Python_Basics/Linear_Regression/.ipynb_checkpoints/Linear_Regression_Python-checkpoint.ipynb

@@ -480,8 +480,9 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python2"
   },