ClassExamples/ShoeSize_Height_Weight_predict_MaleFemale.ipynb

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      "\n",
      "1) Using Decision Tree Prediction is ['female']\n",
      "2) Using K Neighbors Classifier Prediction is ['female']\n",
      "3) Using MLPC Classifier Prediction is ['female']\n",
      "4) Using RandomForestClassifier Prediction is ['female']\n",
      "\n"
     ]
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   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    " \n",
    "#{height, weights, shoe size}\n",
    "X = [[190,70,44],[166,65,45],[190,90,47],[175,64,39],[171,75,40],[177,80,42],[160,60,38],[144,54,37]]\n",
    "Y = ['male','male','male','male','female','female','female','female']\n",
    " \n",
    "#Predict for this vector (height, wieghts, shoe size)\n",
    "#P = [[183,75,25]]\n",
    "#P = [[165,55,40]]  #test for female  for weight 55 or 85 after 25 years\n",
    "#P = [[183,80,30]]   #test for male \n",
    "P =  [[170,56,25]]\n",
    " \n",
    "#{Decision Tree Model}\n",
    "clf = DecisionTreeClassifier()\n",
    "clf = clf.fit(X,Y)\n",
    "print (\"\\n1) Using Decision Tree Prediction is \" + str(clf.predict(P)))\n",
    " \n",
    "#{K Neighbors Classifier}\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X,Y)\n",
    "print (\"2) Using K Neighbors Classifier Prediction is \" + str(knn.predict(P)))\n",
    " \n",
    "#{using MLPClassifier}\n",
    "mlpc = MLPClassifier()\n",
    "mlpc.fit(X,Y)\n",
    "print (\"3) Using MLPC Classifier Prediction is \" + str(mlpc.predict(P)))\n",
    " \n",
    "#{using MLPClassifier}\n",
    "rfor = RandomForestClassifier()\n",
    "rfor.fit(X,Y)\n",
    "print (\"4) Using RandomForestClassifier Prediction is \" + str(rfor.predict(P)) +\"\\n\")\n",
    " "
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