436Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/jg-fisher/diabetesNeuralNetwork/master/prima-indians-diabetes.csv', delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into input (X) and output (Y) variables, splitting csv data\n",
    "\n",
    "\n",
    "X = df.iloc[:,:8]\n",
    "y = df.iloc[:,8]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single Perceptron Layers 10, 50, 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 10 Cross Validation Scores: [64.81481481 64.81481481 67.28971963 60.74766355 76.41509434]\n",
      "MLP with 10 Accuracy Score: 66.23376623376623\n"
     ]
    }
   ],
   "source": [
    "mlp10 = MLPClassifier(hidden_layer_sizes=(10,), random_state=42)\n",
    "mlp10.fit(X_train, y_train)\n",
    "mlp10_predict=mlp10.predict(X_test)\n",
    "mlp10_conf_matrix=confusion_matrix(y_test, mlp10_predict)\n",
    "mlp10_acc_score = accuracy_score(y_test, mlp10_predict)\n",
    "\n",
    "print(\"MLP with 10 Cross Validation Scores:\", cross_val_score(mlp10, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 10 Accuracy Score:\",mlp10_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 50 Cross Validation Scores: [62.96296296 68.51851852 67.28971963 61.68224299 70.75471698]\n",
      "MLP with 50 Accuracy Score: 73.16017316017316\n"
     ]
    }
   ],
   "source": [
    "mlp50 = MLPClassifier(hidden_layer_sizes=(50,), random_state=42)\n",
    "mlp50.fit(X_train, y_train)\n",
    "mlp50_predict=mlp50.predict(X_test)\n",
    "mlp50_conf_matrix=confusion_matrix(y_test, mlp50_predict)\n",
    "mlp50_acc_score = accuracy_score(y_test, mlp50_predict)\n",
    "\n",
    "print(\"MLP with 50 Cross Validation Scores:\", cross_val_score(mlp50, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 50 Accuracy Score:\",mlp50_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 100 Cross Validation Scores: [62.03703704 70.37037037 70.09345794 64.48598131 74.52830189]\n",
      "MLP with 100 Accuracy Score: 71.42857142857143\n"
     ]
    }
   ],
   "source": [
    "mlp100 = MLPClassifier(hidden_layer_sizes=(100,), random_state=42)\n",
    "mlp100.fit(X_train, y_train)\n",
    "mlp100_predict=mlp100.predict(X_test)\n",
    "mlp100_conf_matrix=confusion_matrix(y_test, mlp100_predict)\n",
    "mlp100_acc_score = accuracy_score(y_test, mlp100_predict)\n",
    "\n",
    "print(\"MLP with 100 Cross Validation Scores:\", cross_val_score(mlp100, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 100 Accuracy Score:\",mlp100_acc_score*100)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Scores:\n",
      "MLP with 10 Accuracy Score: 66.23376623376623\n",
      "MLP with 50 Accuracy Score: 73.16017316017316\n",
      "MLP with 100 Accuracy Score: 71.42857142857143\n",
      "\n",
      "Cross Validation Scores:\n",
      "MLP with 10 Cross Validation Scores: [64.81481481 64.81481481 67.28971963 60.74766355 76.41509434]\n",
      "MLP with 50 Cross Validation Scores: [62.96296296 68.51851852 67.28971963 61.68224299 70.75471698]\n",
      "MLP with 100 Cross Validation Scores: [62.03703704 70.37037037 70.09345794 64.48598131 74.52830189]\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Scores:\")\n",
    "print(\"MLP with 10 Accuracy Score:\",mlp10_acc_score*100)\n",
    "print(\"MLP with 50 Accuracy Score:\",mlp50_acc_score*100)\n",
    "print(\"MLP with 100 Accuracy Score:\",mlp100_acc_score*100)\n",
    "\n",
    "print(\"\\nCross Validation Scores:\")\n",
    "print(\"MLP with 10 Cross Validation Scores:\", cross_val_score(mlp10, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 50 Cross Validation Scores:\", cross_val_score(mlp50, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 100 Cross Validation Scores:\", cross_val_score(mlp100, X_train, y_train, cv=5)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Double Perceptron Layers w/ 2nd layer at 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 10,10 Cross Validation Scores: [67.59259259 71.2962963  68.22429907 69.1588785  70.75471698]\n",
      "MLP with 10,10 Accuracy Score: 70.12987012987013\n"
     ]
    }
   ],
   "source": [
    "mlp1010 = MLPClassifier(hidden_layer_sizes=(10,10), random_state=42)\n",
    "mlp1010.fit(X_train, y_train)\n",
    "mlp1010_predict=mlp1010.predict(X_test)\n",
    "mlp1010_conf_matrix=confusion_matrix(y_test, mlp1010_predict)\n",
    "mlp1010_acc_score = accuracy_score(y_test, mlp1010_predict)\n",
    "\n",
    "print(\"MLP with 10,10 Cross Validation Scores:\", cross_val_score(mlp1010, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 10,10 Accuracy Score:\",mlp1010_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 50,10 Cross Validation Scores: [65.74074074 71.2962963  70.09345794 64.48598131 70.75471698]\n",
      "MLP with 50,10 Accuracy Score: 74.89177489177489\n"
     ]
    }
   ],
   "source": [
    "mlp5010 = MLPClassifier(hidden_layer_sizes=(50,10), random_state=42)\n",
    "mlp5010.fit(X_train, y_train)\n",
    "mlp5010_predict=mlp5010.predict(X_test)\n",
    "mlp5010_conf_matrix=confusion_matrix(y_test, mlp5010_predict)\n",
    "mlp5010_acc_score = accuracy_score(y_test, mlp5010_predict)\n",
    "\n",
    "print(\"MLP with 50,10 Cross Validation Scores:\", cross_val_score(mlp5010, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 50,10 Accuracy Score:\",mlp5010_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 100,10 Cross Validation Scores: [60.18518519 69.44444444 64.48598131 65.42056075 71.69811321]\n",
      "MLP with 100,10 Accuracy Score: 69.6969696969697\n"
     ]
    }
   ],
   "source": [
    "# Establish and fit the model, with a single, 100 perceptron layer.\n",
    "mlp10010 = MLPClassifier(hidden_layer_sizes=(100,10), random_state=42)\n",
    "mlp10010.fit(X_train, y_train)\n",
    "mlp10010_predict=mlp10010.predict(X_test)\n",
    "mlp10010_conf_matrix=confusion_matrix(y_test, mlp10010_predict)\n",
    "mlp10010_acc_score = accuracy_score(y_test, mlp10010_predict)\n",
    "\n",
    "print(\"MLP with 100,10 Cross Validation Scores:\", cross_val_score(mlp10010, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 100,10 Accuracy Score:\",mlp10010_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Scores:\n",
      "MLP with 10,10 Accuracy Score: 70.12987012987013\n",
      "MLP with 50,10 Accuracy Score: 74.89177489177489\n",
      "MLP with 100,10 Accuracy Score: 69.6969696969697\n",
      "\n",
      "Cross Validation Scores:\n",
      "MLP with 10,10 Cross Validation Scores: [67.59259259 71.2962963  68.22429907 69.1588785  70.75471698]\n",
      "MLP with 50,10 Cross Validation Scores: [65.74074074 71.2962963  70.09345794 64.48598131 70.75471698]\n",
      "MLP with 100,10 Cross Validation Scores: [60.18518519 69.44444444 64.48598131 65.42056075 71.69811321]\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Scores:\")\n",
    "print(\"MLP with 10,10 Accuracy Score:\",mlp1010_acc_score*100)\n",
    "print(\"MLP with 50,10 Accuracy Score:\",mlp5010_acc_score*100)\n",
    "print(\"MLP with 100,10 Accuracy Score:\",mlp10010_acc_score*100)\n",
    "\n",
    "print(\"\\nCross Validation Scores:\")\n",
    "print(\"MLP with 10,10 Cross Validation Scores:\", cross_val_score(mlp1010, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 50,10 Cross Validation Scores:\", cross_val_score(mlp5010, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 100,10 Cross Validation Scores:\", cross_val_score(mlp10010, X_train, y_train, cv=5)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Double Perceptron Layers w/ 2nd layer at 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 10,50 Cross Validation Scores: [65.74074074 66.66666667 66.35514019 60.74766355 68.86792453]\n",
      "MLP with 10,50 Accuracy Score: 74.45887445887446\n"
     ]
    }
   ],
   "source": [
    "mlp1050 = MLPClassifier(hidden_layer_sizes=(10,50), random_state=42)\n",
    "mlp1050.fit(X_train, y_train)\n",
    "mlp1050_predict=mlp1050.predict(X_test)\n",
    "mlp1050_conf_matrix=confusion_matrix(y_test, mlp1050_predict)\n",
    "mlp1050_acc_score = accuracy_score(y_test, mlp1050_predict)\n",
    "\n",
    "print(\"MLP with 10,50 Cross Validation Scores:\", cross_val_score(mlp1050, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 10,50 Accuracy Score:\",mlp1050_acc_score*100)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 50,50 Cross Validation Scores: [63.88888889 73.14814815 68.22429907 66.35514019 74.52830189]\n",
      "MLP with 50,50 Accuracy Score: 73.59307359307358\n"
     ]
    }
   ],
   "source": [
    "mlp5050 = MLPClassifier(hidden_layer_sizes=(50,50), random_state=42)\n",
    "mlp5050.fit(X_train, y_train)\n",
    "mlp5050_predict=mlp5050.predict(X_test)\n",
    "mlp5050_conf_matrix=confusion_matrix(y_test, mlp5050_predict)\n",
    "mlp5050_acc_score = accuracy_score(y_test, mlp5050_predict)\n",
    "\n",
    "print(\"MLP with 50,50 Cross Validation Scores:\", cross_val_score(mlp5050, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 50,50 Accuracy Score:\",mlp5050_acc_score*100)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 100,50 Cross Validation Scores: [67.59259259 65.74074074 64.48598131 63.55140187 72.64150943]\n",
      "MLP with 100,50 Accuracy Score: 73.16017316017316\n"
     ]
    }
   ],
   "source": [
    "mlp10050 = MLPClassifier(hidden_layer_sizes=(100,50), random_state=42)\n",
    "mlp10050.fit(X_train, y_train)\n",
    "mlp10050_predict=mlp10050.predict(X_test)\n",
    "mlp10050_conf_matrix=confusion_matrix(y_test, mlp10050_predict)\n",
    "mlp10050_acc_score = accuracy_score(y_test, mlp10050_predict)\n",
    "\n",
    "print(\"MLP with 100,50 Cross Validation Scores:\", cross_val_score(mlp10050, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 100,50 Accuracy Score:\",mlp10050_acc_score*100)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Scores:\n",
      "MLP with 10,50 Accuracy Score: 74.45887445887446\n",
      "MLP with 50,50 Accuracy Score: 73.59307359307358\n",
      "MLP with 100,50 Accuracy Score: 73.16017316017316\n",
      "\n",
      "Cross Validation Scores:\n",
      "MLP with 10,50 Cross Validation Scores: [65.74074074 66.66666667 66.35514019 60.74766355 68.86792453]\n",
      "MLP with 50,50 Cross Validation Scores: [63.88888889 73.14814815 68.22429907 66.35514019 74.52830189]\n",
      "MLP with 100,50 Cross Validation Scores: [67.59259259 65.74074074 64.48598131 63.55140187 72.64150943]\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Scores:\")\n",
    "print(\"MLP with 10,50 Accuracy Score:\",mlp1050_acc_score*100)\n",
    "print(\"MLP with 50,50 Accuracy Score:\",mlp5050_acc_score*100)\n",
    "print(\"MLP with 100,50 Accuracy Score:\",mlp10050_acc_score*100)\n",
    "\n",
    "print(\"\\nCross Validation Scores:\")\n",
    "print(\"MLP with 10,50 Cross Validation Scores:\", cross_val_score(mlp1050, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 50,50 Cross Validation Scores:\", cross_val_score(mlp5050, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 100,50 Cross Validation Scores:\", cross_val_score(mlp10050, X_train, y_train, cv=5)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Double Perceptron Layers w/ 2nd layer at 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 10,100 Cross Validation Scores: [61.11111111 66.66666667 70.09345794 61.68224299 68.86792453]\n",
      "MLP with 10,100 Accuracy Score: 72.2943722943723\n"
     ]
    }
   ],
   "source": [
    "mlp10100 = MLPClassifier(hidden_layer_sizes=(10,100), random_state=42)\n",
    "mlp10100.fit(X_train, y_train)\n",
    "mlp10100_predict=mlp10100.predict(X_test)\n",
    "mlp10100_conf_matrix=confusion_matrix(y_test, mlp10100_predict)\n",
    "mlp10100_acc_score = accuracy_score(y_test, mlp10100_predict)\n",
    "\n",
    "print(\"MLP with 10,100 Cross Validation Scores:\", cross_val_score(mlp10100, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 10,100 Accuracy Score:\",mlp10100_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 50,100 Cross Validation Scores: [63.88888889 65.74074074 68.22429907 67.28971963 70.75471698]\n",
      "MLP with 50,100 Accuracy Score: 70.12987012987013\n"
     ]
    }
   ],
   "source": [
    "mlp50100 = MLPClassifier(hidden_layer_sizes=(50,100), random_state=42)\n",
    "mlp50100.fit(X_train, y_train)\n",
    "mlp50100_predict=mlp50100.predict(X_test)\n",
    "mlp50100_conf_matrix=confusion_matrix(y_test, mlp50100_predict)\n",
    "mlp50100_acc_score = accuracy_score(y_test, mlp50100_predict)\n",
    "\n",
    "print(\"MLP with 50,100 Cross Validation Scores:\", cross_val_score(mlp50100, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 50,100 Accuracy Score:\",mlp50100_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 100,100 Cross Validation Scores: [67.59259259 64.81481481 62.61682243 59.81308411 66.03773585]\n",
      "MLP with 100,100 Accuracy Score: 76.19047619047619\n"
     ]
    }
   ],
   "source": [
    "# Establish and fit the model, with a single, 100 perceptron layer.\n",
    "mlp100100 = MLPClassifier(hidden_layer_sizes=(100,100), random_state=42)\n",
    "mlp100100.fit(X_train, y_train)\n",
    "mlp100100_predict=mlp100100.predict(X_test)\n",
    "mlp100100_conf_matrix=confusion_matrix(y_test, mlp100100_predict)\n",
    "mlp100100_acc_score = accuracy_score(y_test, mlp100100_predict)\n",
    "\n",
    "print(\"MLP with 100,100 Cross Validation Scores:\", cross_val_score(mlp100100, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 100,100 Accuracy Score:\",mlp100100_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Scores:\n",
      "MLP with 10,100 Accuracy Score: 72.2943722943723\n",
      "MLP with 50,100 Accuracy Score: 70.12987012987013\n",
      "MLP with 100,100 Accuracy Score: 76.19047619047619\n",
      "\n",
      "Cross Validation Scores:\n",
      "MLP with 10,100 Cross Validation Scores: [61.11111111 66.66666667 70.09345794 61.68224299 68.86792453]\n",
      "MLP with 50,100 Cross Validation Scores: [63.88888889 65.74074074 68.22429907 67.28971963 70.75471698]\n",
      "MLP with 100,100 Cross Validation Scores: [67.59259259 64.81481481 62.61682243 59.81308411 66.03773585]\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Scores:\")\n",
    "print(\"MLP with 10,100 Accuracy Score:\",mlp10100_acc_score*100)\n",
    "print(\"MLP with 50,100 Accuracy Score:\",mlp50100_acc_score*100)\n",
    "print(\"MLP with 100,100 Accuracy Score:\",mlp100100_acc_score*100)\n",
    "\n",
    "print(\"\\nCross Validation Scores:\")\n",
    "print(\"MLP with 10,100 Cross Validation Scores:\", cross_val_score(mlp10100, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 50,100 Cross Validation Scores:\", cross_val_score(mlp50100, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 100,100 Cross Validation Scores:\", cross_val_score(mlp100100, X_train, y_train, cv=5)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Triple Perceptron Layers w/ 2nd and 3rd layers at 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 10,10,10 Cross Validation Scores: [66.66666667 68.51851852 65.42056075 54.20560748 72.64150943]\n",
      "MLP with 10,10,10 Accuracy Score: 72.2943722943723\n"
     ]
    }
   ],
   "source": [
    "mlp101010 = MLPClassifier(hidden_layer_sizes=(10,10,10), random_state=42)\n",
    "mlp101010.fit(X_train, y_train)\n",
    "mlp101010_predict=mlp101010.predict(X_test)\n",
    "mlp101010_conf_matrix=confusion_matrix(y_test, mlp101010_predict)\n",
    "mlp101010_acc_score = accuracy_score(y_test, mlp101010_predict)\n",
    "\n",
    "print(\"MLP with 10,10,10 Cross Validation Scores:\", cross_val_score(mlp101010, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 10,10,10 Accuracy Score:\",mlp101010_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 50,10,10 Cross Validation Scores: [62.96296296 70.37037037 64.48598131 65.42056075 73.58490566]\n",
      "MLP with 50,10,10 Accuracy Score: 76.19047619047619\n"
     ]
    }
   ],
   "source": [
    "mlp501010 = MLPClassifier(hidden_layer_sizes=(50,10,10), random_state=42)\n",
    "mlp501010.fit(X_train, y_train)\n",
    "mlp501010_predict=mlp501010.predict(X_test)\n",
    "mlp501010_conf_matrix=confusion_matrix(y_test, mlp501010_predict)\n",
    "mlp501010_acc_score = accuracy_score(y_test, mlp501010_predict)\n",
    "\n",
    "print(\"MLP with 50,10,10 Cross Validation Scores:\", cross_val_score(mlp501010, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 50,10,10 Accuracy Score:\",mlp501010_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 100,10,10 Cross Validation Scores: [62.96296296 73.14814815 67.28971963 67.28971963 72.64150943]\n",
      "MLP with 100,10,10 Accuracy Score: 72.72727272727273\n"
     ]
    }
   ],
   "source": [
    "# Establish and fit the model, with a single, 100 perceptron layer.\n",
    "mlp1001010 = MLPClassifier(hidden_layer_sizes=(100,10,10), random_state=42)\n",
    "mlp1001010.fit(X_train, y_train)\n",
    "mlp1001010_predict=mlp1001010.predict(X_test)\n",
    "mlp1001010_conf_matrix=confusion_matrix(y_test, mlp1001010_predict)\n",
    "mlp1001010_acc_score = accuracy_score(y_test, mlp1001010_predict)\n",
    "\n",
    "print(\"MLP with 100,10,10 Cross Validation Scores:\", cross_val_score(mlp1001010, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 100,10,10 Accuracy Score:\",mlp1001010_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Scores:\n",
      "MLP with 10,10,10 Accuracy Score: 72.2943722943723\n",
      "MLP with 50,10,10 Accuracy Score: 76.19047619047619\n",
      "MLP with 100,10,10 Accuracy Score: 72.72727272727273\n",
      "\n",
      "Cross Validation Scores:\n",
      "MLP with 10,10,10 Cross Validation Scores: [66.66666667 68.51851852 65.42056075 54.20560748 72.64150943]\n",
      "MLP with 50,10,10 Cross Validation Scores: [62.96296296 70.37037037 64.48598131 65.42056075 73.58490566]\n",
      "MLP with 100,10,10 Cross Validation Scores: [62.96296296 73.14814815 67.28971963 67.28971963 72.64150943]\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Scores:\")\n",
    "print(\"MLP with 10,10,10 Accuracy Score:\",mlp101010_acc_score*100)\n",
    "print(\"MLP with 50,10,10 Accuracy Score:\",mlp501010_acc_score*100)\n",
    "print(\"MLP with 100,10,10 Accuracy Score:\",mlp1001010_acc_score*100)\n",
    "\n",
    "print(\"\\nCross Validation Scores:\")\n",
    "print(\"MLP with 10,10,10 Cross Validation Scores:\", cross_val_score(mlp101010, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 50,10,10 Cross Validation Scores:\", cross_val_score(mlp501010, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 100,10,10 Cross Validation Scores:\", cross_val_score(mlp1001010, X_train, y_train, cv=5)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 layers all even"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 10,10,10 Cross Validation Scores: [66.66666667 68.51851852 65.42056075 54.20560748 72.64150943]\n",
      "MLP with 10,10,10 Accuracy Score: 72.2943722943723\n"
     ]
    }
   ],
   "source": [
    "mlp101010 = MLPClassifier(hidden_layer_sizes=(10,10,10), random_state=42)\n",
    "mlp101010.fit(X_train, y_train)\n",
    "mlp101010_predict=mlp101010.predict(X_test)\n",
    "mlp101010_conf_matrix=confusion_matrix(y_test, mlp101010_predict)\n",
    "mlp101010_acc_score = accuracy_score(y_test, mlp101010_predict)\n",
    "\n",
    "print(\"MLP with 10,10,10 Cross Validation Scores:\", cross_val_score(mlp101010, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 10,10,10 Accuracy Score:\",mlp101010_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 50,50,50 Cross Validation Scores: [60.18518519 68.51851852 70.09345794 65.42056075 66.98113208]\n",
      "MLP with 50,50,50 Accuracy Score: 67.09956709956711\n"
     ]
    }
   ],
   "source": [
    "mlp505050 = MLPClassifier(hidden_layer_sizes=(50,50,50), random_state=42)\n",
    "mlp505050.fit(X_train, y_train)\n",
    "mlp505050_predict=mlp505050.predict(X_test)\n",
    "mlp505050_conf_matrix=confusion_matrix(y_test, mlp505050_predict)\n",
    "mlp505050_acc_score = accuracy_score(y_test, mlp505050_predict)\n",
    "\n",
    "print(\"MLP with 50,50,50 Cross Validation Scores:\", cross_val_score(mlp505050, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 50,50,50 Accuracy Score:\",mlp505050_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 100,100,100 Cross Validation Scores: [62.03703704 63.88888889 70.09345794 61.68224299 72.64150943]\n",
      "MLP with 100,100,100 Accuracy Score: 73.59307359307358\n"
     ]
    }
   ],
   "source": [
    "mlp100100100 = MLPClassifier(hidden_layer_sizes=(100,100,100), random_state=42)\n",
    "mlp100100100.fit(X_train, y_train)\n",
    "mlp100100100_predict=mlp100100100.predict(X_test)\n",
    "mlp100100100_conf_matrix=confusion_matrix(y_test, mlp100100100_predict)\n",
    "mlp100100100_acc_score = accuracy_score(y_test, mlp100100100_predict)\n",
    "\n",
    "print(\"MLP with 100,100,100 Cross Validation Scores:\", cross_val_score(mlp100100100, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 100,100,100 Accuracy Score:\",mlp100100100_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Scores:\n",
      "MLP with 10,10,10 Accuracy Score: 72.2943722943723\n",
      "MLP with 50,50,50 Accuracy Score: 67.09956709956711\n",
      "MLP with 100,100,100 Accuracy Score: 73.59307359307358\n",
      "\n",
      "Cross Validation Scores:\n",
      "MLP with 10,10,10 Cross Validation Scores: [66.66666667 68.51851852 65.42056075 54.20560748 72.64150943]\n",
      "MLP with 50,50,50 Cross Validation Scores: [60.18518519 68.51851852 70.09345794 65.42056075 66.98113208]\n",
      "MLP with 100,100,100 Cross Validation Scores: [62.03703704 63.88888889 70.09345794 61.68224299 72.64150943]\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Scores:\")\n",
    "print(\"MLP with 10,10,10 Accuracy Score:\",mlp101010_acc_score*100)\n",
    "print(\"MLP with 50,50,50 Accuracy Score:\",mlp505050_acc_score*100)\n",
    "print(\"MLP with 100,100,100 Accuracy Score:\",mlp100100100_acc_score*100)\n",
    "\n",
    "print(\"\\nCross Validation Scores:\")\n",
    "print(\"MLP with 10,10,10 Cross Validation Scores:\", cross_val_score(mlp101010, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 50,50,50 Cross Validation Scores:\", cross_val_score(mlp505050, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 100,100,100 Cross Validation Scores:\", cross_val_score(mlp100100100, X_train, y_train, cv=5)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP with 100,150 Cross Validation Scores: [67.59259259 69.44444444 71.02803738 64.48598131 67.9245283 ]\n",
      "MLP with 100,150 Accuracy Score: 70.995670995671\n"
     ]
    }
   ],
   "source": [
    "### Checking 100 with a bigger 2nd layer \n",
    "\n",
    "# Establish and fit the model, with a single, 100 perceptron layer.\n",
    "mlp100150 = MLPClassifier(hidden_layer_sizes=(100,150), random_state=42)\n",
    "mlp100150.fit(X_train, y_train)\n",
    "mlp100150_predict=mlp100150.predict(X_test)\n",
    "mlp100150_conf_matrix=confusion_matrix(y_test, mlp100150_predict)\n",
    "mlp100150_acc_score = accuracy_score(y_test, mlp100150_predict)\n",
    "\n",
    "print(\"MLP with 100,150 Cross Validation Scores:\", cross_val_score(mlp100150, X_train, y_train, cv=5)*100)\n",
    "print(\"MLP with 100,150 Accuracy Score:\",mlp100150_acc_score*100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary? \n",
    "\n",
    "<b>A girl knows nothing. A girl has no conclusion.\n",
    "    \n",
    "    \n",
    "ok ok do the 2nd part...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Random Forest Classifier Accuracy Score: 73.16017316017316\n",
      "Random Forest Classifier F1 Score: 69.18144258908589\n",
      "Random Forest Classifier Precision Score: 70.26795380728979\n",
      "Random Forest Classifier Recall Score: 68.59685430463576\n",
      "Random Forest Classifier Cross Validation Scores: [75.         77.77777778 69.1588785  69.1588785  77.35849057]\n",
      "\n",
      "\n",
      "Random Forest Classifier Confusion Matrix:\n",
      " [[126  25]\n",
      " [ 37  43]]\n"
     ]
    }
   ],
   "source": [
    "#Random Forest\n",
    "#random forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc = RandomForestClassifier(random_state = 42)\n",
    "rfc.fit(X_train, y_train)\n",
    "pred_rfc = rfc.predict(X_test)\n",
    "#Check accuracy\n",
    "\n",
    "print('\\n')\n",
    "print(\"Random Forest Classifier Accuracy Score:\",accuracy_score(y_test,pred_rfc )*100)\n",
    "print(\"Random Forest Classifier F1 Score:\",f1_score(y_test,pred_rfc, average=\"macro\")*100)\n",
    "print(\"Random Forest Classifier Precision Score:\",precision_score(y_test,pred_rfc, average=\"macro\")*100)\n",
    "print(\"Random Forest Classifier Recall Score:\",recall_score(y_test, pred_rfc, average=\"macro\")*100) \n",
    "print(\"Random Forest Classifier Cross Validation Scores:\", cross_val_score(rfc, X_train, y_train, cv=5)*100)\n",
    "print(\"\\n\")\n",
    "print(\"Random Forest Classifier Confusion Matrix:\\n\", confusion_matrix(y_test,pred_rfc))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Gradient Boosting Classifier Accuracy Score: 75.75757575757575\n",
      "Gradient Boosting Classifier F1 Score: 73.06795469686875\n",
      "Gradient Boosting Classifier Precision Score: 73.21518350930117\n",
      "Gradient Boosting Classifier Recall Score: 72.93460264900662\n",
      "Gradient Boosting Classifier Cross Validation Scores: [75.         76.85185185 69.1588785  71.96261682 72.64150943]\n",
      "\n",
      "\n",
      "Gradient Boosting Classifier Confusion Matrix:\n",
      " [[124  27]\n",
      " [ 29  51]]\n"
     ]
    }
   ],
   "source": [
    "#Gradient Boost?\n",
    "from sklearn import ensemble\n",
    "\n",
    "\n",
    "gbc = ensemble.GradientBoostingClassifier(random_state = 42)\n",
    "gbc.fit(X_train, y_train)\n",
    "pred_gbc = gbc.predict(X_test)\n",
    "#Check accuracy\n",
    "\n",
    "print('\\n')\n",
    "print(\"Gradient Boosting Classifier Accuracy Score:\",accuracy_score(y_test,pred_gbc )*100)\n",
    "print(\"Gradient Boosting Classifier F1 Score:\",f1_score(y_test, pred_gbc, average=\"macro\")*100)\n",
    "print(\"Gradient Boosting Classifier Precision Score:\",precision_score(y_test,pred_gbc, average=\"macro\")*100)\n",
    "print(\"Gradient Boosting Classifier Recall Score:\",recall_score(y_test, pred_gbc, average=\"macro\")*100) \n",
    "print(\"Gradient Boosting Classifier Cross Validation Scores:\", cross_val_score(gbc, X_train, y_train, cv=5)*100)\n",
    "print(\"\\n\")\n",
    "print(\"Gradient Boosting Classifier Confusion Matrix:\\n\", confusion_matrix(y_test,pred_gbc))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK OK OK I saw... something...\n",
    "\n",
    "With this, accuracy scores were similar but confusion matricx score were more off. This might matter on larget datasets but in comparison to the simplest classifiers they were similar and it didn't. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 216 candidates, totalling 648 fits\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=42 \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=42, score=0.75, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=42 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=42, score=0.7865168539325843, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=42 \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=42, score=0.7752808988764045, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=200, random_state=42 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=200, random_state=42, score=0.7666666666666667, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=200, random_state=42 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.6s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=200, random_state=42, score=0.7696629213483146, total=   0.2s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=200, random_state=42 \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=200, random_state=42, score=0.7752808988764045, total=   0.2s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=300, random_state=42 \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=300, random_state=42, score=0.7611111111111111, total=   0.3s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=300, random_state=42 \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=300, random_state=42, score=0.7808988764044944, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=300, random_state=42 \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=300, random_state=42, score=0.7865168539325843, total=   0.3s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=100, random_state=42 \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=100, random_state=42, score=0.7611111111111111, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=100, random_state=42 \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=100, random_state=42, score=0.7584269662921348, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=100, random_state=42 \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=100, random_state=42, score=0.7696629213483146, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=200, random_state=42 \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=200, random_state=42, score=0.7666666666666667, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=200, random_state=42 \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=200, random_state=42, score=0.7696629213483146, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=200, random_state=42 \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=200, random_state=42, score=0.7865168539325843, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=300, random_state=42 \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=300, random_state=42, score=0.7666666666666667, total=   0.2s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=300, random_state=42 \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=300, random_state=42, score=0.7696629213483146, total=   0.2s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=4, n_estimators=300, random_state=42 \n",
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     ]
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      "[CV]  criterion=gini, max_depth=10, max_features=log2, min_samples_split=6, n_estimators=300, random_state=42, score=0.7666666666666667, total=   0.2s\n",
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      "[CV]  criterion=gini, max_depth=100, max_features=auto, min_samples_split=6, n_estimators=200, random_state=42, score=0.7584269662921348, total=   0.1s\n",
      "[CV] criterion=gini, max_depth=100, max_features=auto, min_samples_split=6, n_estimators=200, random_state=42 \n",
      "[CV]  criterion=gini, max_depth=100, max_features=auto, min_samples_split=6, n_estimators=200, random_state=42, score=0.7865168539325843, total=   0.1s\n",
      "[CV] criterion=gini, max_depth=100, max_features=auto, min_samples_split=6, n_estimators=300, random_state=42 \n",
      "[CV]  criterion=gini, max_depth=100, max_features=auto, min_samples_split=6, n_estimators=300, random_state=42, score=0.7555555555555555, total=   0.6s\n",
      "[CV] criterion=gini, max_depth=100, max_features=auto, min_samples_split=6, n_estimators=300, random_state=42 \n",
      "[CV]  criterion=gini, max_depth=100, max_features=auto, min_samples_split=6, n_estimators=300, random_state=42, score=0.7584269662921348, total=   0.2s\n",
      "[CV] criterion=gini, max_depth=100, max_features=auto, min_samples_split=6, n_estimators=300, random_state=42 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=gini, max_depth=100, max_features=auto, min_samples_split=6, n_estimators=300, random_state=42, score=0.7752808988764045, total=   0.2s\n",
      "[CV] criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=100, random_state=42 \n",
      "[CV]  criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=100, random_state=42, score=0.7666666666666667, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=100, random_state=42 \n",
      "[CV]  criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=100, random_state=42, score=0.7696629213483146, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=100, random_state=42 \n",
      "[CV]  criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=100, random_state=42, score=0.7640449438202247, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=200, random_state=42 \n",
      "[CV]  criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=200, random_state=42, score=0.7555555555555555, total=   0.1s\n",
      "[CV] criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=200, random_state=42 \n",
      "[CV]  criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=200, random_state=42, score=0.7640449438202247, total=   0.1s\n",
      "[CV] criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=200, random_state=42 \n",
      "[CV]  criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=200, random_state=42, score=0.7696629213483146, total=   0.1s\n",
      "[CV] criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=300, random_state=42 \n",
      "[CV]  criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=300, random_state=42, score=0.7555555555555555, total=   0.2s\n",
      "[CV] criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=300, random_state=42 \n",
      "[CV]  criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=300, random_state=42, score=0.7528089887640449, total=   0.2s\n",
      "[CV] criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=300, random_state=42 \n",
      "[CV]  criterion=gini, max_depth=100, max_features=auto, min_samples_split=8, n_estimators=300, random_state=42, score=0.7640449438202247, total=   0.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 648 out of 648 | elapsed:  2.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok\n"
     ]
    }
   ],
   "source": [
    "#randomforestparam\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'criterion': ['entropy', 'gini'],\n",
    "    'max_depth': [10, 50, 100],\n",
    "    'min_samples_split': [2, 4, 6, 8],\n",
    "    'max_features': ['log2', 'sqrt','auto'],\n",
    "    'random_state':[42]}\n",
    "\n",
    "grid_rfc = GridSearchCV(rfc, param_grid = param_grid, cv = 3, verbose = 5)\n",
    "grid_rfc.fit(X_train,y_train)\n",
    "\n",
    "rfcbest_estimator = grid_rfc.best_estimator_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "       estimator=GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=4,\n",
       "              max_features='sqrt', max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_sam...        subsample=1, tol=0.0001, validation_fraction=0.1, verbose=0,\n",
       "              warm_start=False),\n",
       "       fit_params=None, iid=False, n_jobs=4,\n",
       "       param_grid={'learning_rate': [0.15, 0.1, 0.05, 0.01, 0.005, 0.001], 'n_estimators': [100, 250, 500], 'max_depth': [4, 6, 8], 'min_samples_leaf': [20, 50, 100, 150], 'max_features': [1.0, 0.3, 0.1], 'random_state': [42]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#gradientboostparam\n",
    "gbcparam= {'learning_rate':[0.15,0.1,0.05,0.01,0.005,0.001], \n",
    "           'n_estimators':[100,250,500,],\n",
    "           'max_depth': [4, 6, 8],\n",
    "           'min_samples_leaf': [20, 50,100,150],\n",
    "           'max_features': [1.0, 0.3, 0.1],\n",
    "          'random_state':[42]}\n",
    "\n",
    "gbctuning = GridSearchCV(estimator = ensemble.GradientBoostingClassifier(max_depth=4, min_samples_split=2, min_samples_leaf=1, subsample=1,max_features='sqrt', random_state=10), \n",
    "            param_grid = gbcparam, scoring='accuracy',n_jobs=4,iid=False, cv=3)\n",
    "\n",
    "\n",
    "gbctuning.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters:\n",
      "\n",
      "\n",
      "Random Forest:\n",
      " {'criterion': 'gini', 'max_depth': 50, 'max_features': 'log2', 'min_samples_split': 2, 'n_estimators': 200, 'random_state': 42}\n",
      "\n",
      "\n",
      "Gradient Boost:\n",
      " {'learning_rate': 0.1, 'max_depth': 8, 'max_features': 1.0, 'min_samples_leaf': 20, 'n_estimators': 100, 'random_state': 42}\n"
     ]
    }
   ],
   "source": [
    "print('Best Parameters:')\n",
    "print('\\n')\n",
    "print(\"Random Forest:\\n\",grid_rfc.best_params_)\n",
    "print('\\n')\n",
    "print(\"Gradient Boost:\\n\",gbctuning.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Random Forest Classifier Accuracy Score: 75.75757575757575\n",
      "Random Forest Classifier F1 Score: 73.22847682119205\n",
      "Random Forest Classifier Precision Score: 73.22847682119206\n",
      "Random Forest Classifier Recall Score: 73.22847682119206\n",
      "Random Forest Classifier Cross Validation Scores: [77.77777778 79.21348315 79.21348315]\n",
      "\n",
      "\n",
      "Random Forest Classifier Confusion Matrix:\n",
      " [[123  28]\n",
      " [ 28  52]]\n"
     ]
    }
   ],
   "source": [
    "bestrfc = RandomForestClassifier(criterion= 'gini', max_depth= 50, \n",
    "                                 max_features= 'log2', min_samples_split= 2, \n",
    "                                 n_estimators= 200, random_state= 42)\n",
    "bestrfc.fit(X_train, y_train)\n",
    "bestpred_rfc = bestrfc.predict(X_test)\n",
    "#Check accuracy\n",
    "\n",
    "print('\\n')\n",
    "print(\"Random Forest Classifier Accuracy Score:\",accuracy_score(y_test,bestpred_rfc )*100)\n",
    "print(\"Random Forest Classifier F1 Score:\",f1_score(y_test, bestpred_rfc, average=\"macro\")*100)\n",
    "print(\"Random Forest Classifier Precision Score:\",precision_score(y_test,bestpred_rfc, average=\"macro\")*100)\n",
    "print(\"Random Forest Classifier Recall Score:\",recall_score(y_test, bestpred_rfc, average=\"macro\")*100) \n",
    "print(\"Random Forest Classifier Cross Validation Scores:\", cross_val_score(bestrfc, X_train, y_train, cv=3)*100)\n",
    "print(\"\\n\")\n",
    "print(\"Random Forest Classifier Confusion Matrix:\\n\", confusion_matrix(y_test,bestpred_rfc))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Gradient Boosting Classifier Accuracy Score: 76.19047619047619\n",
      "Gradient Boosting Classifier F1 Score: 74.21090023343145\n",
      "Gradient Boosting Classifier Precision Score: 73.85057471264368\n",
      "Gradient Boosting Classifier Recall Score: 74.73509933774834\n",
      "Gradient Boosting Classifier Cross Validation Scores: [77.77777778 78.08988764 75.28089888]\n",
      "\n",
      "\n",
      "Gradient Boosting Classifier Confusion Matrix:\n",
      " [[120  31]\n",
      " [ 24  56]]\n"
     ]
    }
   ],
   "source": [
    "bestgbc = ensemble.GradientBoostingClassifier(learning_rate= 0.1, max_depth= 8, \n",
    "                                              max_features= 1.0, min_samples_leaf= 20, \n",
    "                                              n_estimators= 100, random_state= 42)\n",
    "bestgbc.fit(X_train, y_train)\n",
    "bestpred_gbc = bestgbc.predict(X_test)\n",
    "#Check accuracy\n",
    "print('\\n')\n",
    "print(\"Gradient Boosting Classifier Accuracy Score:\",accuracy_score(y_test,bestpred_gbc )*100)\n",
    "print(\"Gradient Boosting Classifier F1 Score:\",f1_score(y_test, bestpred_gbc, average=\"macro\")*100)\n",
    "print(\"Gradient Boosting Classifier Precision Score:\",precision_score(y_test,bestpred_gbc, average=\"macro\")*100)\n",
    "print(\"Gradient Boosting Classifier Recall Score:\",recall_score(y_test, bestpred_gbc, average=\"macro\")*100) \n",
    "print(\"Gradient Boosting Classifier Cross Validation Scores:\", cross_val_score(bestgbc, X_train, y_train, cv=3)*100)\n",
    "print(\"\\n\")\n",
    "print(\"Gradient Boosting Classifier Confusion Matrix:\\n\", confusion_matrix(y_test,bestpred_gbc))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scores better with the right parameters but you have to take the time to find those."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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