neural_net_keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from keras import backend as K\n",
    "from sklearn import metrics\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_validate\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "import statistics as s\n",
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the file\n",
    "def reading_file(path,columns):\n",
    "    df=pd.read_csv(path)\n",
    "    df.columns=columns\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data preprocessing\n",
    "def data_preprocessing(df):\n",
    "    values={}\n",
    "    #converting the value to float \n",
    "    df=df.astype(float)\n",
    "    #finding the mean of each column and storing the values in the dic\n",
    "    for i in df.columns:\n",
    "        mean=df[i].mean()\n",
    "        values[i]=mean \n",
    "    #replacing the Nan value by mean\n",
    "    df=df.fillna(value=values)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# spliting dataset into training and testing\n",
    "def spliting(df):\n",
    "    number_of_parameter=len(df.columns)\n",
    "    dataset=df.values\n",
    "    y=dataset[:,0]\n",
    "    x=dataset[:,0:number_of_parameter]\n",
    "    x.shape[1] \n",
    "    return x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create model\n",
    "def model_perceptron(input_dim=None):\n",
    "    model= Sequential()\n",
    "    # perceptron\n",
    "    model.add(Dense(units=1,input_dim=input_dim,activation='relu'))\n",
    "    #setting the optimizer value\n",
    "    keras.optimizers.Adam(lr=0.0001,decay=0.00001)\n",
    "    #compiling the model\n",
    "    model.compile(optimizer='adam', loss=\"mean_absolute_error\")\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating model\n",
    "def model_neural(input_dim=None):\n",
    "    model= Sequential()\n",
    "    #hidden layer\n",
    "    model.add(Dense(units=input_dim,input_dim=input_dim,activation='relu'))\n",
    "    # output layer\n",
    "    model.add(Dense(1))\n",
    "    # setting the learning rate and decay value\n",
    "    keras.optimizers.Adam(lr=0.0001,decay=0.00001)\n",
    "    #compiling the model\n",
    "    model.compile(optimizer='adam', loss=\"mean_absolute_error\")\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating model\n",
    "def model_neural_xl(input_dim=None):\n",
    "    model= Sequential()\n",
    "    #hidden layer\n",
    "    model.add(Dense(units=5,input_dim=input_dim))\n",
    "    #2nd hidden layer\n",
    "    model.add(Dense(units=3,activation='relu'))\n",
    "    # output layer\n",
    "    model.add(Dense(1))\n",
    "    # setting the learning rate and decay value\n",
    "    keras.optimizers.Adam(lr=0.0001,decay=0.00001)\n",
    "    #compiling the model\n",
    "    model.compile(optimizer='adam', loss=\"mean_absolute_error\")\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_selection_perceptron(x,y):\n",
    "    # declaring value\n",
    "    r_square_list=[]\n",
    "    adjusted_r_squared_list=[]\n",
    "    cross_rsquare_list=[]\n",
    "    r_square=0\n",
    "    adjusted_r_squared=0\n",
    "    x1=x\n",
    "    f=[] \n",
    "    # for selection of n feature\n",
    "    for i in range (x1.shape[1]):\n",
    "        max=0\n",
    "        #for selection of n feature\n",
    "        for j in range (x1.shape[1]):\n",
    "            # selecting that are not been selected\n",
    "            if (j not in f):\n",
    "                # taking the required column value\n",
    "                x_sel=x1[:,j]\n",
    "                # Taking the value of the feature that are already been selected\n",
    "                x_done=x1[:,f]\n",
    "                # joining both the numpy array\n",
    "                x_sel=np.concatenate((x_done,x_sel[:,None]),axis=1)\n",
    "                # creating model\n",
    "                model=model_perceptron(input_dim=x_sel.shape[1])\n",
    "                # Trainig the model\n",
    "                result=model.fit(x_sel,y,epochs=100,batch_size=5)\n",
    "                # calculating the y pred\n",
    "                y_pred=model.predict(x_sel)\n",
    "                # calculating R_square and adjusted R_square\n",
    "                r_square=metrics.r2_score(y,y_pred)\n",
    "                adjusted_r_squared=1 - (1-r_square)*(len(y)-1)/(len(y)-x.shape[1]-1)\n",
    "                # checking wheather a particular feature is the best or not\n",
    "                if(max<r_square):\n",
    "                    max=r_square\n",
    "                    index=j\n",
    "        f.append(index)\n",
    "        # Creation of model for cross_validation\n",
    "        model = KerasRegressor(build_fn=model_neural,input_dim=x[:,f].shape[1],epochs=100, batch_size=5, verbose=0)\n",
    "        # calculating rsqaure cv\n",
    "        c_r=cross_val_score(model, x1[:,f], y, cv=10, scoring='r2')\n",
    "        # storing value for rsquare_cv, adjusted_rsquare, rsquare\n",
    "        cross_rsquare_list.append(s.mean(c_r))\n",
    "        r_square_list.append(r_square)\n",
    "        adjusted_r_squared_list.append(adjusted_r_squared)\n",
    "    return r_square_list,adjusted_r_squared_list,cross_rsquare_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_selection_neural(x,y):\n",
    "    # declaring value\n",
    "    r_square_list=[]\n",
    "    adjusted_r_squared_list=[]\n",
    "    cross_rsquare_list=[]\n",
    "    r_square=0\n",
    "    adjusted_r_squared=0\n",
    "    x1=x\n",
    "    f=[] \n",
    "    # for selection of n feature\n",
    "    for i in range (x1.shape[1]):\n",
    "        max=0\n",
    "        #for selection of n feature\n",
    "        for j in range (x1.shape[1]):\n",
    "            # selecting that are not been selected\n",
    "            if (j not in f):\n",
    "                # taking the required column value\n",
    "                x_sel=x1[:,j]\n",
    "                # Taking the value of the feature that are already been selected\n",
    "                x_done=x1[:,f]\n",
    "                # joining both the numpy array\n",
    "                x_sel=np.concatenate((x_done,x_sel[:,None]),axis=1)\n",
    "                # creating model\n",
    "                model=model_neural(input_dim=x_sel.shape[1])\n",
    "                # Trainig the model\n",
    "                result=model.fit(x_sel,y,epochs=100,batch_size=5)\n",
    "                # calculating the y pred\n",
    "                y_pred=model.predict(x_sel)\n",
    "                # calculating R_square and adjusted R_square\n",
    "                r_square=metrics.r2_score(y,y_pred)\n",
    "                adjusted_r_squared=1 - (1-r_square)*(len(y)-1)/(len(y)-x.shape[1]-1)\n",
    "                # checking wheather a particular feature is the best or not\n",
    "                if(max<r_square):\n",
    "                    max=r_square\n",
    "                    index=j\n",
    "        f.append(index)\n",
    "        # Creation of model for cross_validation\n",
    "        model = KerasRegressor(build_fn=model_neural,input_dim=x[:,f].shape[1],epochs=100, batch_size=5, verbose=0)\n",
    "        # calculating rsqaure cv\n",
    "        c_r=cross_val_score(model, x1[:,f], y, cv=10, scoring='r2')\n",
    "        # storing value for rsquare_cv, adjusted_rsquare, rsquare\n",
    "        cross_rsquare_list.append(s.mean(c_r))\n",
    "        r_square_list.append(r_square)\n",
    "        adjusted_r_squared_list.append(adjusted_r_squared)\n",
    "    return r_square_list,adjusted_r_squared_list,cross_rsquare_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot R^2\n",
    "def plot_graph(rsquare,adjusted_rsquare,rsquare_cv):    \n",
    "    plt.plot(rsquare,color='red')\n",
    "    plt.plot(rsquare_cv,color='green')\n",
    "    plt.plot(adjusted_rsquare,color='blue')\n",
    "    plt.title(\"rsquare, adjusted, r_cv\")\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making a user defined program\n",
    "def auto_mpg():\n",
    "    filename=\"auto-mpg.csv\"\n",
    "    columns=[\"mpg\", \"cylinders\", \"displacement\", \"horsepower\", \"weight\", \"acceleration\", \"model_year\", \"origin\"]\n",
    "    return filename,columns\n",
    "    \n",
    "def gps():\n",
    "    filename=\"gps.csv\"\n",
    "    columns=[\"id\",\"id_android\",\"speed\",\"time\",\"distance\",\"rating\",\"rating_bus\",\"rating_weather\",\"car_or_bus\"]\n",
    "    return filename,columns\n",
    "    \n",
    "def real_estate():\n",
    "    filename=\"real_estate.csv\"\n",
    "    columns=[\"X1 transaction date\",\"X2 house age\",\"X3 distance to the nearest MRT station\",\"X4 number of convenience stores\",\"X5 latitude\",\"X6 longitude\",\"Y house price of unit area\"]\n",
    "    return filename,columns\n",
    "    \n",
    "def Concrete_Data():\n",
    "    filename=\"Concrete_Data.csv\"\n",
    "    columns=[\"Cement\", \"Blast furnace slag\", \"Fly Ash\", \"Water\", \"Superplasticizer\", \"Coarse Aggregate\", \"Fine Aggregate\", \"Age\", \"Concrete compressive strength\"]\n",
    "    return filename,columns\n",
    "\n",
    "def Admission_Predict():\n",
    "    filename=\"Admission_Predict.csv\"\n",
    "    columns=[\"GRE Score\",\"TOEFL Score\",\"University Rating\",\"SOP\",\"LOR\",\"CGPA\",\"Research\",\"Chance of Admit \"]\n",
    "    return filename,columns\n",
    "\n",
    "def computer_hardware():\n",
    "    filename=\"computerhardware.csv\"\n",
    "    columns=[\"erp\",\"myct\",\"mmin\",\"mmax\",\"cach\", \"chmin\",\"chmax\",\"prp\"]\n",
    "    return filename,columns\n",
    "\n",
    "def beijing_pm():\n",
    "    filename=\"beijing_pm.csv\"\n",
    "    columns=[\"pm2.5\",\"year\",\"month\",\"day\",\"hour\",\"DEWP\",\"TEMP\",\"PRES\",\"lws\",\"ls\",\"lr\"]\n",
    "    return filename,columns\n",
    "\n",
    "def wine():\n",
    "    filename=\"wine.csv\"\n",
    "    columns=[\"quality\",\"fixed acidity\",\"volatile acidity\",\"citric acid\",\"residual sugar\",\"chlorides\",\"free sulfur dioxide\",\"total sulfur dioxide\",\"density\",\"pH\",\"sulphates\",\"alcohol\"]\n",
    "    return filename,columns\n",
    "\n",
    "def red_wine():\n",
    "    filename=\"red_wine.csv\"\n",
    "    columns=[\"quality\",\"fixed acidity\",\"volatile acidity\",\"citric acid\",\"residual sugar\",\"chlorides\",\"free sulfur dioxide\",\"total sulfur dioxide\",\"density\",\"pH\",\"sulphates\",\"alcohol\"]\n",
    "    return filename,columns\n",
    "\n",
    "def yachtdata():\n",
    "    filename=\"yachtdata.csv\"\n",
    "    columns=[\"Longitudnal_position\",\" Prismatic_coef\",\" Length_displacement\",\" Beam_draught\",\" length_beam\",\" froude_number\",\" residuary_resistance\"]\n",
    "    return filename,columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choice function\n",
    "def choice(argument):\n",
    "    options={\n",
    "            1:auto_mpg(),\n",
    "            2:gps(),\n",
    "            3:real_estate(),\n",
    "            4:Concrete_Data(),\n",
    "            5:Admission_Predict(),\n",
    "            6:computer_hardware(),\n",
    "            7:beijing_pm(),\n",
    "            8:wine(),\n",
    "            9:red_wine(),\n",
    "            10:yachtdata()}\n",
    "    filename,columns=options.get(argument,\"invalid input\")\n",
    "    return filename,columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "::::::::::::::::::::::::::::::\n",
      "\n",
      " enter path of dataset  =/home/anant/data-science_2/Project_2_keras_implementation/dataset/\n",
      "\n",
      "::::::::::::::::::::::::::::::\n",
      "\n",
      "::::::Choose the dataset::::::\n",
      "\n",
      " 1. auto-mpg \n",
      " 2. GPS Trajectories  \n",
      " 3. Real estate valuation data set \n",
      " 4. Concrete Data \n",
      " 5. Admission Predict \n",
      " 6. Computer Hardware \n",
      " 7. Beijing PM \n",
      " 8. White Wine \n",
      " 9. Red Wine \n",
      " 10. Yatch Data \n",
      " 11. Anyother dataset\n",
      "\n",
      "::::::::::::::::::::::::::::::\n",
      "\n",
      " enter your choice =2\n",
      "\n",
      "::::::::::::::::::::::::::::::\n",
      "\n",
      "choose the model\n",
      "\n",
      " 1. perceptron \n",
      " 2. Neural-net-3-layer \n",
      " 3. Neural-net-x-layer\n",
      "enter the choice=1\n",
      "Epoch 1/100\n",
      "162/162 [==============================] - 5s 31ms/step - loss: 900.0554\n",
      "Epoch 2/100\n",
      "162/162 [==============================] - 0s 796us/step - loss: 445.5314\n",
      "Epoch 3/100\n",
      "162/162 [==============================] - 0s 704us/step - loss: 57.2254\n",
      "Epoch 4/100\n",
      "162/162 [==============================] - 0s 723us/step - loss: 5.7656\n",
      "Epoch 5/100\n",
      "162/162 [==============================] - 0s 686us/step - loss: 11.9660\n",
      "Epoch 6/100\n",
      "162/162 [==============================] - 0s 804us/step - loss: 5.8112\n",
      "Epoch 7/100\n",
      "162/162 [==============================] - 0s 754us/step - loss: 2.8506\n",
      "Epoch 8/100\n",
      "162/162 [==============================] - 0s 769us/step - loss: 3.4342\n",
      "Epoch 9/100\n",
      "162/162 [==============================] - 0s 789us/step - loss: 2.3347\n",
      "Epoch 10/100\n",
      "162/162 [==============================] - 0s 868us/step - loss: 3.5591\n",
      "Epoch 11/100\n",
      "162/162 [==============================] - 0s 664us/step - loss: 1.7230\n",
      "Epoch 12/100\n",
      "162/162 [==============================] - 0s 663us/step - loss: 3.1590\n",
      "Epoch 13/100\n",
      "162/162 [==============================] - 0s 788us/step - loss: 2.7009\n",
      "Epoch 14/100\n",
      "162/162 [==============================] - 0s 758us/step - loss: 0.9180\n",
      "Epoch 15/100\n",
      "162/162 [==============================] - 0s 703us/step - loss: 1.5057\n",
      "Epoch 16/100\n",
      "162/162 [==============================] - 0s 649us/step - loss: 4.1075\n",
      "Epoch 17/100\n",
      "162/162 [==============================] - 0s 710us/step - loss: 9.8130\n",
      "Epoch 18/100\n",
      "162/162 [==============================] - 0s 734us/step - loss: 2.3303\n",
      "Epoch 19/100\n",
      "162/162 [==============================] - 0s 720us/step - loss: 2.3158\n",
      "Epoch 20/100\n",
      "162/162 [==============================] - 0s 708us/step - loss: 2.5746\n",
      "Epoch 21/100\n",
      "162/162 [==============================] - 0s 636us/step - loss: 1.8222\n",
      "Epoch 22/100\n",
      "162/162 [==============================] - 0s 743us/step - loss: 5.0254\n",
      "Epoch 23/100\n",
      "162/162 [==============================] - 0s 698us/step - loss: 3.6767\n",
      "Epoch 24/100\n",
      "162/162 [==============================] - 0s 700us/step - loss: 4.9246\n",
      "Epoch 25/100\n",
      "162/162 [==============================] - 0s 768us/step - loss: 2.1896\n",
      "Epoch 26/100\n",
      "162/162 [==============================] - 0s 729us/step - loss: 1.6977\n",
      "Epoch 27/100\n",
      "162/162 [==============================] - 0s 717us/step - loss: 2.1176\n",
      "Epoch 28/100\n",
      "162/162 [==============================] - 0s 734us/step - loss: 1.9460\n",
      "Epoch 29/100\n",
      "162/162 [==============================] - 0s 726us/step - loss: 7.0343\n",
      "Epoch 30/100\n",
      "162/162 [==============================] - 0s 650us/step - loss: 2.1372\n",
      "Epoch 31/100\n",
      "162/162 [==============================] - 0s 726us/step - loss: 5.1121\n",
      "Epoch 32/100\n",
      "162/162 [==============================] - 0s 738us/step - loss: 3.1282\n",
      "Epoch 33/100\n",
      "162/162 [==============================] - 0s 668us/step - loss: 7.0214\n",
      "Epoch 34/100\n",
      "162/162 [==============================] - 0s 731us/step - loss: 8.5788\n",
      "Epoch 35/100\n",
      "162/162 [==============================] - 0s 747us/step - loss: 2.2477\n",
      "Epoch 36/100\n",
      "162/162 [==============================] - 0s 686us/step - loss: 4.0689\n",
      "Epoch 37/100\n",
      "162/162 [==============================] - 0s 635us/step - loss: 2.2663\n",
      "Epoch 38/100\n",
      "162/162 [==============================] - 0s 671us/step - loss: 6.1813\n",
      "Epoch 39/100\n",
      "162/162 [==============================] - 0s 745us/step - loss: 2.4245\n",
      "Epoch 40/100\n",
      "162/162 [==============================] - 0s 750us/step - loss: 1.5419\n",
      "Epoch 41/100\n",
      "162/162 [==============================] - 0s 704us/step - loss: 1.5937\n",
      "Epoch 42/100\n",
      "162/162 [==============================] - 0s 638us/step - loss: 4.7523\n",
      "Epoch 43/100\n",
      "162/162 [==============================] - 0s 715us/step - loss: 4.0507\n",
      "Epoch 44/100\n",
      "162/162 [==============================] - 0s 610us/step - loss: 3.7435\n",
      "Epoch 45/100\n",
      "162/162 [==============================] - 0s 724us/step - loss: 1.8672\n",
      "Epoch 46/100\n",
      "162/162 [==============================] - 0s 647us/step - loss: 2.7812\n",
      "Epoch 47/100\n",
      "162/162 [==============================] - 0s 727us/step - loss: 2.8423\n",
      "Epoch 48/100\n",
      "162/162 [==============================] - 0s 741us/step - loss: 10.0491\n",
      "Epoch 49/100\n",
      "162/162 [==============================] - 0s 760us/step - loss: 5.7410\n",
      "Epoch 50/100\n",
      "162/162 [==============================] - 0s 675us/step - loss: 2.5161\n",
      "Epoch 51/100\n",
      "162/162 [==============================] - 0s 804us/step - loss: 2.7862\n",
      "Epoch 52/100\n",
      "162/162 [==============================] - 0s 672us/step - loss: 4.9451\n",
      "Epoch 53/100\n",
      "162/162 [==============================] - 0s 741us/step - loss: 2.9225\n",
      "Epoch 54/100\n",
      "162/162 [==============================] - 0s 783us/step - loss: 1.4296\n",
      "Epoch 55/100\n",
      "162/162 [==============================] - 0s 709us/step - loss: 1.9858\n",
      "Epoch 56/100\n",
      "162/162 [==============================] - 0s 727us/step - loss: 3.3784\n",
      "Epoch 57/100\n",
      "162/162 [==============================] - 0s 755us/step - loss: 5.7663\n",
      "Epoch 58/100\n",
      "162/162 [==============================] - 0s 735us/step - loss: 2.4695\n",
      "Epoch 59/100\n",
      "162/162 [==============================] - 0s 737us/step - loss: 5.2201\n",
      "Epoch 60/100\n",
      "162/162 [==============================] - 0s 673us/step - loss: 2.6566\n",
      "Epoch 61/100\n",
      "162/162 [==============================] - 0s 820us/step - loss: 1.4991\n",
      "Epoch 62/100\n",
      "162/162 [==============================] - 0s 698us/step - loss: 2.1574\n",
      "Epoch 63/100\n",
      "162/162 [==============================] - 0s 740us/step - loss: 2.1449\n",
      "Epoch 64/100\n",
      "162/162 [==============================] - 0s 685us/step - loss: 1.2869\n",
      "Epoch 65/100\n",
      "162/162 [==============================] - 0s 730us/step - loss: 2.6373\n",
      "Epoch 66/100\n",
      "162/162 [==============================] - 0s 616us/step - loss: 1.0614\n",
      "Epoch 67/100\n",
      "162/162 [==============================] - 0s 740us/step - loss: 2.7028\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# main function        \n",
    "def main():\n",
    "    print(\"\\n::::::::::::::::::::::::::::::\")\n",
    "    path= input(\"\\n enter path of dataset  =\")\n",
    "    print(\"\\n::::::::::::::::::::::::::::::\\n\")\n",
    "    print(\"::::::Choose the dataset::::::\")\n",
    "    print(\"\\n 1. auto-mpg \\n 2. GPS Trajectories  \\n 3. Real estate valuation data set \\n 4. Concrete Data \\n 5. Admission Predict \\n 6. Computer Hardware \\n 7. Beijing PM \\n 8. White Wine \\n 9. Red Wine \\n 10. Yatch Data \\n 11. Anyother dataset\")\n",
    "    print(\"\\n::::::::::::::::::::::::::::::\")\n",
    "    choice_value= int(input(\"\\n enter your choice =\"))\n",
    "    print(\"\\n::::::::::::::::::::::::::::::\\n\")\n",
    "    if(choice_value>0 and choice_value<11):\n",
    "        filename,columns=choice(choice_value)\n",
    "    if(choice_value==11):\n",
    "        filename=input(\"enter the filename\")\n",
    "        columns=input(\"Enter column name and separate it with comma(,)\")\n",
    "        columns=columns.split(',')\n",
    "    if(choice_value<=0 or choice_value>=12):\n",
    "        print(\"************invalid choice**************\")\n",
    "    \n",
    "    #full path of dataset\n",
    "    path=path+filename\n",
    "    #reading file and putting into dataframe\n",
    "    dataframe=reading_file(path,columns)\n",
    "    #pre processing\n",
    "    dataframe=data_preprocessing(dataframe)\n",
    "    #spliting the dataset into predictors and target\n",
    "    x,y=spliting(dataframe)\n",
    "    #model selection and implementation\n",
    "    print(\"choose the model\")\n",
    "    print(\"\\n 1. perceptron \\n 2. Neural-net-3-layer \\n 3. Neural-net-x-layer\")\n",
    "    model_choice=int(input(\"enter the choice=\"))\n",
    "    if(model_choice==1):\n",
    "        rsqaure,adjusted,cross=forward_selection_perceptron(x,y)\n",
    "    if(model_choice==2):\n",
    "        rsqaure,adjusted,cross=forward_selection_neural(x,y)\n",
    "    if(model_choice==3):\n",
    "        rsqaure,adjusted,cross=forward_selection_neural_xl(x,y)\n",
    "    if(model_choice<=0 or model_choice>=4):\n",
    "        print(\"************invalid choice**************\")\n",
    "    #Plotting graph of model\n",
    "    plot_graph(rsqaure,adjusted,cross)\n",
    "    \n",
    "#path=/home/anant/data-science_2/Project_2_keras_implementation/dataset/\n",
    "\n",
    "        \n",
    "if __name__ == '__main__':\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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