@@ -0,0 +1,1049 @@
+#!/usr/bin/env python%0A# rgb2_color_k_nearest.py%0A# by wilsonmar@gmail.com, ayush.original@gmail.com, paarth.n@gmail.com%0A# This is not the complete/correct approach. This is just the framework possibly of using ML%0Aimport numpy as np%0Afrom sklearn import preprocessing, cross_validation, neighbors%0Aimport pandas as pd%0A%0Adf = pd.read_csv('rgb_combined_v01.csv') # load into dataframe%0Adf.drop(%5B'_Hex','_Name','_grey','_X11','_SVG'%5D, 1, inplace=True)  #axis=1 denotes that we are referring to a column, not a row%0A#Here I've dropped all columns except those which give RGB values%0A%0A#TEST: Data Loaded%0Aprint (df)%0A%0A%0AX= np.array(df.drop(%5B'_Title'%5D,1))# represents features%0AY= np.array(df%5B'_Title'%5D)# represents labels%0A%0A#partition in training and testing sets%0AX_train, X_test , Y_train, Y_test = cross_validation.train_test_split(X,Y,test_size=0.01)%0A%0Aclf = neighbors.KNeighborsClassifier()%0Aclf.fit(X_test, Y_test)%0A%0Aaccuracy = clf.score(X_test, Y_test)%0Aprint(accuracy)%0A%0Aexample_rgb = np.array(%5B222,184,135%5D) %0Aprediction = clf.predict(example_rgb)%0Aprint(prediction)

Add basic ML framework- does not work though