Country Development Project.ipynb

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     "text": [
      "WELCOME TO THE COUNTRY DEVELOPMENT CLASSIFYING MODEL! \n",
      "Enter Country Name: India\n",
      "             City   Population( in million)  Sanitation  \\\n",
      "0        New Delhi                     11.1         9.1   \n",
      "1           Mumbai                     12.2         8.1   \n",
      "2             Pune                      3.1         8.4   \n",
      "3        Bengaluru                      8.4         8.6   \n",
      "4          Kolkata                      4.4         7.5   \n",
      "5        Hyderabad                      6.7         8.6   \n",
      "6         Warangal                      0.8         6.2   \n",
      "7           Bhopal                      1.7         5.5   \n",
      "8          Lucknow                      2.8         4.3   \n",
      "9      Bhubaneswar                      0.8         8.3   \n",
      "10          Mysore                      0.9         9.2   \n",
      "11          Jaipur                      3.0         7.9   \n",
      "12           Surat                      4.4         7.1   \n",
      "13       Allahabad                      1.1         3.2   \n",
      "14        Durgapur                      0.5         7.4   \n",
      "15          Rajkot                      1.2         7.0   \n",
      "16       Ahmedabad                      5.5         9.4   \n",
      "17          Indore                      1.9         9.6   \n",
      "18         Guwhati                      0.9         8.2   \n",
      "19          Nagpur                      2.4         8.4   \n",
      "20       Ghaziabad                      1.6         4.5   \n",
      "21      Chandigarh                      0.9         9.0   \n",
      "22        Ludhiana                      1.6         8.1   \n",
      "23       Jalandhar                      0.9         7.6   \n",
      "24      Jamshedpur                      0.6         7.3   \n",
      "25           Patna                      1.6         7.0   \n",
      "26          Ranchi                      1.0         7.4   \n",
      "27      Trivandrum                      0.7         8.2   \n",
      "28         Gwalior                      1.0         7.1   \n",
      "29        Srinagar                      1.1         8.6   \n",
      "30          Meerut                      1.3         6.1   \n",
      "31        Gurugram                      0.8         7.5   \n",
      "32           Noida                      0.6         6.9   \n",
      "33       Faridabad                      1.4         7.0   \n",
      "34        Dehradun                      0.5         8.1   \n",
      "35          Nashik                      1.4         7.5   \n",
      "36          Raipur                      1.0         7.7   \n",
      "37         Chennai                      4.6         8.0   \n",
      "38  Vishakhapatnam                      1.7         8.2   \n",
      "39         Dhanbad                      1.1         6.5   \n",
      "40           Kochi                      0.6         7.8   \n",
      "41        Varanasi                      1.1         5.4   \n",
      "42            Agra                      1.5         5.1   \n",
      "43          Kanpur                      2.7         5.9   \n",
      "44        Amritsar                      1.1         7.2   \n",
      "45          Shimla                      0.2         8.5   \n",
      "46        Vadodara                      1.6         7.9   \n",
      "47      Aurangabad                      1.1         7.3   \n",
      "48      Coimbatore                      1.0         8.0   \n",
      "\n",
      "    Mobility & Public Transport  Water &Electricity Supply Safety of Citizens  \\\n",
      "0                           9.8                        9.1                6.4   \n",
      "1                           9.1                        8.9                8.2   \n",
      "2                           8.8                        8.2                  9   \n",
      "3                           8.9                        8.7                8.8   \n",
      "4                           9.6                        9.5                8.4   \n",
      "5                           9.2                        9.3                8.9   \n",
      "6                           6.4                        8.2                  9   \n",
      "7                           7.5                        8.4                9.1   \n",
      "8                           8.0                        8.5                7.9   \n",
      "9                           8.2                        8.0                8.7   \n",
      "10                          7.6                        7.9                8.5   \n",
      "11                          8.6                        8.5                8.3   \n",
      "12                          8.1                        8.3                  8   \n",
      "13                          7.7                        8.1                7.2   \n",
      "14                          8.0                        8.3                7.5   \n",
      "15                          8.1                        7.9             -99999   \n",
      "16                          9.0                        8.7                8.6   \n",
      "17                          8.5                        9.0                8.2   \n",
      "18                          7.6                        9.1                  8   \n",
      "19                          8.1                        8.2                8.5   \n",
      "20                          8.4                        7.8                6.1   \n",
      "21                          8.9                        8.7                8.3   \n",
      "22                          7.7                        7.1                7.4   \n",
      "23                          6.8                        6.9                6.7   \n",
      "24                          8.2                        8.3                  8   \n",
      "25                          8.6                        8.5                7.9   \n",
      "26                          8.4                        8.5                8.1   \n",
      "27                          8.0                        8.6                8.6   \n",
      "28                          7.2                        7.5                7.9   \n",
      "29                          7.8                        8.0                  5   \n",
      "30                          6.7                        6.9                5.9   \n",
      "31                          8.5                        8.7                7.1   \n",
      "32                          8.4                        8.4                6.8   \n",
      "33                          8.1                        7.8                7.6   \n",
      "34                          7.2                        8.2                8.5   \n",
      "35                          7.0                        7.9                7.8   \n",
      "36                          8.3                        8.3                8.2   \n",
      "37                          9.2                        9.1                8.9   \n",
      "38                          8.9                        8.7                8.8   \n",
      "39                          7.1                        8.0                4.8   \n",
      "40                          8.2                        8.2                8.6   \n",
      "41                          7.4                        8.4                8.2   \n",
      "42                          8.1                        7.6                7.3   \n",
      "43                          8.2                        8.0                7.4   \n",
      "44                          8.5                        8.3                8.1   \n",
      "45                          8.0                        7.5                8.8   \n",
      "46                          7.6                        7.8                8.4   \n",
      "47                          7.5                        6.8                  8   \n",
      "48                          7.3                        7.7                8.2   \n",
      "\n",
      "   Connectivity & Digitization Health & Education Development Status  \n",
      "0                          9.5                9.8          Developed  \n",
      "1                          9.6                  9          Developed  \n",
      "2                          8.7                9.1          Developed  \n",
      "3                          9.4                9.6          Developed  \n",
      "4                          9.2                9.5          Developed  \n",
      "5                          9.4                8.7          Developed  \n",
      "6                          7.8                8.2      Not Developed  \n",
      "7                            8                7.5      Not Developed  \n",
      "8                          8.8                8.6          Developed  \n",
      "9                          8.7                8.6          Developed  \n",
      "10                         8.3                8.4      Not Developed  \n",
      "11                           9                8.4          Developed  \n",
      "12                         8.2                7.8      Not Developed  \n",
      "13                         8.3                7.2      Not Developed  \n",
      "14                         7.6                  8      Not Developed  \n",
      "15                         7.8                6.9      Not Developed  \n",
      "16                           9                8.4          Developed  \n",
      "17                         8.4                8.7          Developed  \n",
      "18                         7.5                8.1      Not Developed  \n",
      "19                         8.7                  8          Developed  \n",
      "20                         8.4                7.6      Not Developed  \n",
      "21                         9.1                8.8          Developed  \n",
      "22                      -99999                6.7      Not Developed  \n",
      "23                         7.8                6.3      Not Developed  \n",
      "24                         8.7                  8          Developed  \n",
      "25                           9                8.4          Developed  \n",
      "26                         8.8                8.2          Developed  \n",
      "27                         8.3                8.5          Developed  \n",
      "28                         7.8                7.1      Not Developed  \n",
      "29                         7.5                  4      Not Developed  \n",
      "30                         7.2                5.4      Not Developed  \n",
      "31                           9                8.3          Developed  \n",
      "32                         8.6                8.5      Not Developed  \n",
      "33                         8.7                  8      Not Developed  \n",
      "34                         7.9                8.3      Not Developed  \n",
      "35                         8.2                7.6      Not Developed  \n",
      "36                         8.5                7.8          Developed  \n",
      "37                         9.3                9.4          Developed  \n",
      "38                         8.9                9.2          Developed  \n",
      "39                         7.6                  6      Not Developed  \n",
      "40                         7.9                7.5          Developed  \n",
      "41                         7.5                8.6      Not Developed  \n",
      "42                           8                7.7      Not Developed  \n",
      "43                         8.4                8.3      Not Developed  \n",
      "44                         8.5                7.8          Developed  \n",
      "45                         7.9             -99999          Developed  \n",
      "46                         7.5                7.2      Not Developed  \n",
      "47                         7.3                6.5      Not Developed  \n",
      "48                         7.9             -99999      Not Developed  \n",
      "\n",
      "\n",
      "[[9.1, 9.8, 9.1, '6.4', '9.5', '9.8', 'Developed'], [8.1, 9.1, 8.9, '8.2', '9.6', '9', 'Developed'], [8.4, 8.8, 8.2, '9', '8.7', '9.1', 'Developed'], [8.6, 8.9, 8.7, '8.8', '9.4', '9.6', 'Developed'], [7.5, 9.6, 9.5, '8.4', '9.2', '9.5', 'Developed'], [8.6, 9.2, 9.3, '8.9', '9.4', '8.7', 'Developed'], [6.2, 6.4, 8.2, '9', '7.8', '8.2', 'Not Developed'], [5.5, 7.5, 8.4, '9.1', '8', '7.5', 'Not Developed'], [4.3, 8.0, 8.5, '7.9', '8.8', '8.6', 'Developed'], [8.3, 8.2, 8.0, '8.7', '8.7', '8.6', 'Developed'], [9.2, 7.6, 7.9, '8.5', '8.3', '8.4', 'Not Developed'], [7.9, 8.6, 8.5, '8.3', '9', '8.4', 'Developed'], [7.1, 8.1, 8.3, '8', '8.2', '7.8', 'Not Developed'], [3.2, 7.7, 8.1, '7.2', '8.3', '7.2', 'Not Developed'], [7.4, 8.0, 8.3, '7.5', '7.6', '8', 'Not Developed'], [7.0, 8.1, 7.9, -99999, '7.8', '6.9', 'Not Developed'], [9.4, 9.0, 8.7, '8.6', '9', '8.4', 'Developed'], [9.6, 8.5, 9.0, '8.2', '8.4', '8.7', 'Developed'], [8.2, 7.6, 9.1, '8', '7.5', '8.1', 'Not Developed'], [8.4, 8.1, 8.2, '8.5', '8.7', '8', 'Developed'], [4.5, 8.4, 7.8, '6.1', '8.4', '7.6', 'Not Developed'], [9.0, 8.9, 8.7, '8.3', '9.1', '8.8', 'Developed'], [8.1, 7.7, 7.1, '7.4', -99999, '6.7', 'Not Developed'], [7.6, 6.8, 6.9, '6.7', '7.8', '6.3', 'Not Developed'], [7.3, 8.2, 8.3, '8', '8.7', '8', 'Developed'], [7.0, 8.6, 8.5, '7.9', '9', '8.4', 'Developed'], [7.4, 8.4, 8.5, '8.1', '8.8', '8.2', 'Developed'], [8.2, 8.0, 8.6, '8.6', '8.3', '8.5', 'Developed'], [7.1, 7.2, 7.5, '7.9', '7.8', '7.1', 'Not Developed'], [8.6, 7.8, 8.0, '5', '7.5', '4', 'Not Developed'], [6.1, 6.7, 6.9, '5.9', '7.2', '5.4', 'Not Developed'], [7.5, 8.5, 8.7, '7.1', '9', '8.3', 'Developed'], [6.9, 8.4, 8.4, '6.8', '8.6', '8.5', 'Not Developed'], [7.0, 8.1, 7.8, '7.6', '8.7', '8', 'Not Developed'], [8.1, 7.2, 8.2, '8.5', '7.9', '8.3', 'Not Developed'], [7.5, 7.0, 7.9, '7.8', '8.2', '7.6', 'Not Developed'], [7.7, 8.3, 8.3, '8.2', '8.5', '7.8', 'Developed'], [8.0, 9.2, 9.1, '8.9', '9.3', '9.4', 'Developed'], [8.2, 8.9, 8.7, '8.8', '8.9', '9.2', 'Developed'], [6.5, 7.1, 8.0, '4.8', '7.6', '6', 'Not Developed'], [7.8, 8.2, 8.2, '8.6', '7.9', '7.5', 'Developed'], [5.4, 7.4, 8.4, '8.2', '7.5', '8.6', 'Not Developed'], [5.1, 8.1, 7.6, '7.3', '8', '7.7', 'Not Developed'], [5.9, 8.2, 8.0, '7.4', '8.4', '8.3', 'Not Developed'], [7.2, 8.5, 8.3, '8.1', '8.5', '7.8', 'Developed'], [8.5, 8.0, 7.5, '8.8', '7.9', -99999, 'Developed'], [7.9, 7.6, 7.8, '8.4', '7.5', '7.2', 'Not Developed'], [7.3, 7.5, 6.8, '8', '7.3', '6.5', 'Not Developed'], [8.0, 7.3, 7.7, '8.2', '7.9', -99999, 'Not Developed']]\n",
      "\n",
      "\n",
      "Full data size: 49\n",
      "\n",
      "\n",
      "Training data size: 40\n",
      "\n",
      "\n",
      "Testing data size: 9\n",
      "\n",
      "\n",
      "{'Developed': [[7.9, 8.6, 8.5, '8.3', '9', '8.4'], [7.5, 8.5, 8.7, '7.1', '9', '8.3'], [8.1, 9.1, 8.9, '8.2', '9.6', '9'], [7.0, 8.6, 8.5, '7.9', '9', '8.4'], [8.6, 8.9, 8.7, '8.8', '9.4', '9.6'], [8.2, 8.0, 8.6, '8.6', '8.3', '8.5'], [4.3, 8.0, 8.5, '7.9', '8.8', '8.6'], [8.5, 8.0, 7.5, '8.8', '7.9', -99999], [9.6, 8.5, 9.0, '8.2', '8.4', '8.7'], [9.4, 9.0, 8.7, '8.6', '9', '8.4'], [9.1, 9.8, 9.1, '6.4', '9.5', '9.8'], [8.3, 8.2, 8.0, '8.7', '8.7', '8.6'], [7.3, 8.2, 8.3, '8', '8.7', '8'], [8.0, 9.2, 9.1, '8.9', '9.3', '9.4'], [7.8, 8.2, 8.2, '8.6', '7.9', '7.5'], [9.0, 8.9, 8.7, '8.3', '9.1', '8.8'], [7.7, 8.3, 8.3, '8.2', '8.5', '7.8'], [8.4, 8.1, 8.2, '8.5', '8.7', '8'], [7.5, 9.6, 9.5, '8.4', '9.2', '9.5'], [8.4, 8.8, 8.2, '9', '8.7', '9.1']], 'Not Developed': [[6.5, 7.1, 8.0, '4.8', '7.6', '6'], [8.1, 7.2, 8.2, '8.5', '7.9', '8.3'], [8.6, 7.8, 8.0, '5', '7.5', '4'], [8.2, 7.6, 9.1, '8', '7.5', '8.1'], [5.5, 7.5, 8.4, '9.1', '8', '7.5'], [6.1, 6.7, 6.9, '5.9', '7.2', '5.4'], [7.6, 6.8, 6.9, '6.7', '7.8', '6.3'], [9.2, 7.6, 7.9, '8.5', '8.3', '8.4'], [7.9, 7.6, 7.8, '8.4', '7.5', '7.2'], [7.0, 8.1, 7.9, -99999, '7.8', '6.9'], [4.5, 8.4, 7.8, '6.1', '8.4', '7.6'], [7.5, 7.0, 7.9, '7.8', '8.2', '7.6'], [7.4, 8.0, 8.3, '7.5', '7.6', '8'], [7.1, 7.2, 7.5, '7.9', '7.8', '7.1'], [5.9, 8.2, 8.0, '7.4', '8.4', '8.3'], [5.1, 8.1, 7.6, '7.3', '8', '7.7'], [7.3, 7.5, 6.8, '8', '7.3', '6.5'], [6.2, 6.4, 8.2, '9', '7.8', '8.2'], [7.1, 8.1, 8.3, '8', '8.2', '7.8'], [8.0, 7.3, 7.7, '8.2', '7.9', -99999]]}\n",
      "\n",
      "\n",
      "The State of the Country is Developed\n",
      "\n",
      "\n",
      "Maximum Accuracy Of Model is: 1.0\n",
      "\n",
      "\n",
      "Value of k: 3\n",
      "\n",
      "\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from collections import Counter\n",
    "\n",
    "\n",
    "print(\"WELCOME TO THE COUNTRY DEVELOPMENT CLASSIFYING MODEL! \")\n",
    "\n",
    "\n",
    "cname = input('Enter Country Name: ')\n",
    "\n",
    "\n",
    "df = pd.read_csv(cname+'.csv') \n",
    "df.replace('?',-99999,inplace = True)#replacing missing values denoted by '?' with -99999 in df variable itself\n",
    "print(df)\n",
    "print('\\n')\n",
    "df.drop(['City '],axis=1,inplace=True)\n",
    "df.drop(['Population( in million)'],axis=1,inplace=True)\n",
    "full_data = df.astype(float,errors='ignore').values.tolist()\n",
    "print(full_data)\n",
    "print('\\n')\n",
    "\n",
    "\n",
    "import random\n",
    "random.shuffle(full_data)\n",
    "test_size = 0.2\n",
    "train_data = full_data[:-int(test_size*len(full_data))]\n",
    "test_data = full_data[-int(test_size*len(full_data)):]\n",
    "print('Full data size: {}'.format(len(full_data)))\n",
    "print('\\n')\n",
    "print(\"Training data size: {}\".format(len(train_data)))\n",
    "print('\\n')\n",
    "print(\"Testing data size: {}\".format(len(test_data)))\n",
    "print('\\n')\n",
    "\n",
    "\n",
    "train_set = {'Developed':[] , 'Not Developed':[]} #Training Set\n",
    "test_set = {'Developed':[] , 'Not Developed':[]} #Testing Set\n",
    "for i in train_data:\n",
    "    train_set[i[-1]].append(i[:-1])    \n",
    "for i in test_data:\n",
    "    test_set[i[-1]].append(i[:-1]) \n",
    "print(train_set)\n",
    "print('\\n')\n",
    "\n",
    "\n",
    "def knn(data,predict,k=3): #KNN FUNCTIOM\n",
    "    distances = []\n",
    "    for group in data:\n",
    "        for features in data[group]:\n",
    "            distance = math.sqrt((features[0]-predict[0])**2 +(features[1]-predict[1])**2)\n",
    "            distances.append([distance,group])\n",
    "    votes = [i[1] for i in sorted(distances)[0:k]]\n",
    "    vote_result = Counter(votes).most_common(1)[0][0]\n",
    "    return vote_result\n",
    "\n",
    "datax=[8.2,8.4,7.9,8.2,8.5,8.7] \n",
    "\n",
    "b= knn(train_set,datax,5)\n",
    "print(f\"The State of the Country is {b}\")\n",
    "print('\\n')\n",
    "\n",
    "accuracy = [] #ACCURACY LIST\n",
    "z = [i for i in range(1,100,2)] #list of all values of k used below\n",
    "for k in range(1,100,2):\n",
    "    correct = 0\n",
    "    total = len(test_data)\n",
    "    for group in test_set:\n",
    "        for data in test_set[group]: #data contains a list of 9 features x1,x2...x9\n",
    "            vote = knn(train_set,data,k) #we check classification made by knn for data variable\n",
    "            if group == vote: #if group of data variable = classification made by knn then correct += 1\n",
    "                correct+=1\n",
    "    accuracy.append(correct/total)\n",
    "\n",
    "    \n",
    "print(\"Maximum Accuracy Of Model is: {}\".format(max(accuracy))) #Printing maximum accuracy  \n",
    "print('\\n')\n",
    "print(\"Value of k: {}\".format(z[accuracy.index(max(accuracy))])) #First value of k for which it occurs\n",
    "print('\\n')\n",
    "      \n",
    "#PLOTTING ACCURACY VS. K graph \n",
    "plt.xlabel(\"k\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.plot(z,accuracy) \n",
    "plt.show() #shows the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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