{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Flood prediction Model","metadata":{}},{"cell_type":"code","source":"#Import some basic libraries\nimport numpy as np\nimport pandas as pd","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.059515Z","iopub.execute_input":"2021-08-08T04:28:48.059825Z","iopub.status.idle":"2021-08-08T04:28:48.064116Z","shell.execute_reply.started":"2021-08-08T04:28:48.059795Z","shell.execute_reply":"2021-08-08T04:28:48.063092Z"},"trusted":true},"execution_count":52,"outputs":[]},{"cell_type":"markdown","source":"# Data Insight","metadata":{}},{"cell_type":"code","source":"#Read the data present in dataset\n##data = pd.read_csv('../input/kerela-flood/kerala.csv')\n\ndata = pd.read_csv('../input/my-district-dataset/FLOOD PREDICTION MALAYSIA.csv')\n#data = pd.read_csv('../input/simpleflooddata/Flood_Rain_Simple_Data.csv')\n\n#Using data.head() we can see the top 5 rows of the dataset\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.072735Z","iopub.execute_input":"2021-08-08T04:28:48.073102Z","iopub.status.idle":"2021-08-08T04:28:48.111350Z","shell.execute_reply.started":"2021-08-08T04:28:48.073065Z","shell.execute_reply":"2021-08-08T04:28:48.110345Z"},"trusted":true},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"   STATE  DISTRICT  YEAR     JAN     FEB     MAR     APR     MAY     JUN  \\\n0    108    108001  2000  158.83  162.37  210.68  192.51  214.73  157.55   \n1    108    108001  2001  159.10   41.71  174.50  220.56  177.65  105.61   \n2    108    108001  2002   61.25   50.34   88.15  207.13  115.01   96.08   \n3    108    108001  2003   82.88  118.04  193.40  100.36  101.07  166.81   \n4    108    108001  2004  119.30   71.16  120.80  138.74  120.27  146.03   \n\n      JUL     AUG     SEP     OCT      0V     DEC  ANNUAL RAINFALL  FLOOD   \n0   98.80  165.63  289.14  388.77  313.59  213.60          2566.19       0  \n1  166.59  193.88  206.40  298.14  232.54  150.82          2127.50       1  \n2  115.78  111.12  285.96  206.94  261.33  264.61          1863.70       1  \n3  167.61  270.87  238.84  682.07  251.46  182.35          2555.77       1  \n4  145.35  172.92  222.61  360.21  187.22  168.79          1973.39       1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>STATE</th>\n      <th>DISTRICT</th>\n      <th>YEAR</th>\n      <th>JAN</th>\n      <th>FEB</th>\n      <th>MAR</th>\n      <th>APR</th>\n      <th>MAY</th>\n      <th>JUN</th>\n      <th>JUL</th>\n      <th>AUG</th>\n      <th>SEP</th>\n      <th>OCT</th>\n      <th>0V</th>\n      <th>DEC</th>\n      <th>ANNUAL RAINFALL</th>\n      <th>FLOOD</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>108</td>\n      <td>108001</td>\n      <td>2000</td>\n      <td>158.83</td>\n      <td>162.37</td>\n      <td>210.68</td>\n      <td>192.51</td>\n      <td>214.73</td>\n      <td>157.55</td>\n      <td>98.80</td>\n      <td>165.63</td>\n      <td>289.14</td>\n      <td>388.77</td>\n      <td>313.59</td>\n      <td>213.60</td>\n      <td>2566.19</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>108</td>\n      <td>108001</td>\n      <td>2001</td>\n      <td>159.10</td>\n      <td>41.71</td>\n      <td>174.50</td>\n      <td>220.56</td>\n      <td>177.65</td>\n      <td>105.61</td>\n      <td>166.59</td>\n      <td>193.88</td>\n      <td>206.40</td>\n      <td>298.14</td>\n      <td>232.54</td>\n      <td>150.82</td>\n      <td>2127.50</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>108</td>\n      <td>108001</td>\n      <td>2002</td>\n      <td>61.25</td>\n      <td>50.34</td>\n      <td>88.15</td>\n      <td>207.13</td>\n      <td>115.01</td>\n      <td>96.08</td>\n      <td>115.78</td>\n      <td>111.12</td>\n      <td>285.96</td>\n      <td>206.94</td>\n      <td>261.33</td>\n      <td>264.61</td>\n      <td>1863.70</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>108</td>\n      <td>108001</td>\n      <td>2003</td>\n      <td>82.88</td>\n      <td>118.04</td>\n      <td>193.40</td>\n      <td>100.36</td>\n      <td>101.07</td>\n      <td>166.81</td>\n      <td>167.61</td>\n      <td>270.87</td>\n      <td>238.84</td>\n      <td>682.07</td>\n      <td>251.46</td>\n      <td>182.35</td>\n      <td>2555.77</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>108</td>\n      <td>108001</td>\n      <td>2004</td>\n      <td>119.30</td>\n      <td>71.16</td>\n      <td>120.80</td>\n      <td>138.74</td>\n      <td>120.27</td>\n      <td>146.03</td>\n      <td>145.35</td>\n      <td>172.92</td>\n      <td>222.61</td>\n      <td>360.21</td>\n      <td>187.22</td>\n      <td>168.79</td>\n      <td>1973.39</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#Now we will cheak if any colomns is left empty\ndata.apply(lambda x:sum(x.isnull()), axis=0)","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.114067Z","iopub.execute_input":"2021-08-08T04:28:48.114562Z","iopub.status.idle":"2021-08-08T04:28:48.127952Z","shell.execute_reply.started":"2021-08-08T04:28:48.114502Z","shell.execute_reply":"2021-08-08T04:28:48.126873Z"},"trusted":true},"execution_count":54,"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"STATE              0\nDISTRICT           0\nYEAR               0\nJAN                0\nFEB                0\nMAR                0\nAPR                0\nMAY                0\nJUN                0\nJUL                0\nAUG                0\nSEP                0\nOCT                0\n0V                 0\nDEC                0\nANNUAL RAINFALL    0\nFLOOD              0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"#We want the data in numbers, therefore we will replace the yes/no in floods coloumn by 1/0\n#data['FLOOD'].replace(['YES','NO'],[1,0],inplace=True)\n#data['FLOODS'].replace(['Yes','No'],[1,0],inplace=True)\n#print('done')","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.130075Z","iopub.execute_input":"2021-08-08T04:28:48.130474Z","iopub.status.idle":"2021-08-08T04:28:48.138603Z","shell.execute_reply.started":"2021-08-08T04:28:48.130434Z","shell.execute_reply":"2021-08-08T04:28:48.137546Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"markdown","source":"New data no need to convert\n","metadata":{}},{"cell_type":"code","source":"#Let's see how are data looks like now\n#data.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.140992Z","iopub.execute_input":"2021-08-08T04:28:48.141637Z","iopub.status.idle":"2021-08-08T04:28:48.148294Z","shell.execute_reply.started":"2021-08-08T04:28:48.141586Z","shell.execute_reply":"2021-08-08T04:28:48.147413Z"},"trusted":true},"execution_count":56,"outputs":[]},{"cell_type":"code","source":"#Now let's seperate the data which we are gonna use for prediction\n\nx = data.iloc[:,0:16]\nx.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.151564Z","iopub.execute_input":"2021-08-08T04:28:48.152373Z","iopub.status.idle":"2021-08-08T04:28:48.177567Z","shell.execute_reply.started":"2021-08-08T04:28:48.152304Z","shell.execute_reply":"2021-08-08T04:28:48.176516Z"},"trusted":true},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"   STATE  DISTRICT  YEAR     JAN     FEB     MAR     APR     MAY     JUN  \\\n0    108    108001  2000  158.83  162.37  210.68  192.51  214.73  157.55   \n1    108    108001  2001  159.10   41.71  174.50  220.56  177.65  105.61   \n2    108    108001  2002   61.25   50.34   88.15  207.13  115.01   96.08   \n3    108    108001  2003   82.88  118.04  193.40  100.36  101.07  166.81   \n4    108    108001  2004  119.30   71.16  120.80  138.74  120.27  146.03   \n\n      JUL     AUG     SEP     OCT      0V     DEC  ANNUAL RAINFALL  \n0   98.80  165.63  289.14  388.77  313.59  213.60          2566.19  \n1  166.59  193.88  206.40  298.14  232.54  150.82          2127.50  \n2  115.78  111.12  285.96  206.94  261.33  264.61          1863.70  \n3  167.61  270.87  238.84  682.07  251.46  182.35          2555.77  \n4  145.35  172.92  222.61  360.21  187.22  168.79          1973.39  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>STATE</th>\n      <th>DISTRICT</th>\n      <th>YEAR</th>\n      <th>JAN</th>\n      <th>FEB</th>\n      <th>MAR</th>\n      <th>APR</th>\n      <th>MAY</th>\n      <th>JUN</th>\n      <th>JUL</th>\n      <th>AUG</th>\n      <th>SEP</th>\n      <th>OCT</th>\n      <th>0V</th>\n      <th>DEC</th>\n      <th>ANNUAL RAINFALL</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>108</td>\n      <td>108001</td>\n      <td>2000</td>\n      <td>158.83</td>\n      <td>162.37</td>\n      <td>210.68</td>\n      <td>192.51</td>\n      <td>214.73</td>\n      <td>157.55</td>\n      <td>98.80</td>\n      <td>165.63</td>\n      <td>289.14</td>\n      <td>388.77</td>\n      <td>313.59</td>\n      <td>213.60</td>\n      <td>2566.19</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>108</td>\n      <td>108001</td>\n      <td>2001</td>\n      <td>159.10</td>\n      <td>41.71</td>\n      <td>174.50</td>\n      <td>220.56</td>\n      <td>177.65</td>\n      <td>105.61</td>\n      <td>166.59</td>\n      <td>193.88</td>\n      <td>206.40</td>\n      <td>298.14</td>\n      <td>232.54</td>\n      <td>150.82</td>\n      <td>2127.50</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>108</td>\n      <td>108001</td>\n      <td>2002</td>\n      <td>61.25</td>\n      <td>50.34</td>\n      <td>88.15</td>\n      <td>207.13</td>\n      <td>115.01</td>\n      <td>96.08</td>\n      <td>115.78</td>\n      <td>111.12</td>\n      <td>285.96</td>\n      <td>206.94</td>\n      <td>261.33</td>\n      <td>264.61</td>\n      <td>1863.70</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>108</td>\n      <td>108001</td>\n      <td>2003</td>\n      <td>82.88</td>\n      <td>118.04</td>\n      <td>193.40</td>\n      <td>100.36</td>\n      <td>101.07</td>\n      <td>166.81</td>\n      <td>167.61</td>\n      <td>270.87</td>\n      <td>238.84</td>\n      <td>682.07</td>\n      <td>251.46</td>\n      <td>182.35</td>\n      <td>2555.77</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>108</td>\n      <td>108001</td>\n      <td>2004</td>\n      <td>119.30</td>\n      <td>71.16</td>\n      <td>120.80</td>\n      <td>138.74</td>\n      <td>120.27</td>\n      <td>146.03</td>\n      <td>145.35</td>\n      <td>172.92</td>\n      <td>222.61</td>\n      <td>360.21</td>\n      <td>187.22</td>\n      <td>168.79</td>\n      <td>1973.39</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#Now seperate the flood label from the dataset\n##Changed to seperate to no of occurance\ny = data.iloc[:, -1]\ny.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.179610Z","iopub.execute_input":"2021-08-08T04:28:48.180277Z","iopub.status.idle":"2021-08-08T04:28:48.190936Z","shell.execute_reply.started":"2021-08-08T04:28:48.180206Z","shell.execute_reply":"2021-08-08T04:28:48.189614Z"},"trusted":true},"execution_count":58,"outputs":[{"execution_count":58,"output_type":"execute_result","data":{"text/plain":"0    0\n1    1\n2    1\n3    1\n4    1\nName: FLOOD , dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"#Let's see how the rainfall index vary during rainy season\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\nc = data[['JUN','JUL','AUG','SEP']]\nc.hist()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.193439Z","iopub.execute_input":"2021-08-08T04:28:48.193993Z","iopub.status.idle":"2021-08-08T04:28:48.694451Z","shell.execute_reply.started":"2021-08-08T04:28:48.193908Z","shell.execute_reply":"2021-08-08T04:28:48.693266Z"},"trusted":true},"execution_count":59,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 4 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAYAAAAEICAYAAABWJCMKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAZKklEQVR4nO3df5Ac5X3n8fcnskx0Ij4gMltCUrzkImwgCpBSMA5UsgEThHFF5A87Io4j3dmlPw5XILVVRiRVl9w5JPJdmcQ5G5cVfunKxAplsFHhFDGW2fNxZwtLmANkoUM2MuwhkG1MQIpDsvibP/rZMKxmd2dnpqd7+vm8qqZ2+unumW/3PLPf6e7n6UcRgZmZ5ecnqg7AzMyq4QRgZpYpJwAzs0w5AZiZZcoJwMwsU04AZmaZcgIwM8uUE0ANSJqQ9EFJmyQ92Gb+IUnvTM9vlxSSzm+Z/3OS3KHDhkoX9f5PBh9lszkBDKcXAH8ZzKwnTgDDaTvwC5J+tepAzGx4OQEMp38A/hS4oepAzGx4OQEMr08DPyPp8qoDMbPh5ARQL1PA4jbli4F/bi2IiFeAj6SHyg/NrDQd13vrLyeAenma4lf9v/5Dl/RvgFOB77ZZ/jbg3wK/OZjwzEqx0HpvfeIEUC+7gX8Etkj6SUlLga3AHtp8ESJiCvhj4LpBBmnWZ53W+0Vp/vTjjVUE2yROAPUR6bTOFcAYMAl8BzgNeG/MPnDDZ4HDA4nQrP8WUu+3AD9qeXxlsKE2jzwgTPUkPQz8l4j4QtWxmA2K6331fARQMUlnA2cC36w6FrNBcb2vByeACkn6KPAl4LqI8MUuy4LrfX34FJCZWaZ8BGBmlqk3VB0AwLJly2J0dLTqMPru2LFjLF26tOowKjeo/bB3797vR8SbS3+jPql7vW9a/W3a9kDvdb4WCWB0dJQ9e/ZUHUbfTUxMMDY2VnUYlRvUfpA0VOeT617vm1Z/m7Y90Hud9ykgM7NMOQGYmWXKCcDMLFO1uAbQb6NbvrjgdQ5tvaKESMwGo5s6D673uWtkAuiGv0CWo7nq/fiaKTa1me863xw+BWRmliknADOzTDkBmJllygnAzCxTTgBmZplyK6AeuRWFmQ0rHwGYmWXKCcDMLFNOAGZmmXICMDPLlBOAmVmm5k0Akm6VdETS4y1lfyzp/0t6JD3e1TLvekkHJR2QdFlZgZuZWW86OQK4HVjXpvzPI+Lc9PhbAElnARuAs9M6N0la1K9gzcysf+ZNABHxVeCFDl9vPbAjIl6JiKeAg8D5PcRnZmYl6aUj2Ick/S6wBxiPiB8CK4CvtywzmcqOI2kzsBlgZGSEiYmJHkJ5vfE1U317rV6MLGkfSz+3dRgcPXo0u202GwbdJoBPAR8BIv39GPAfALVZNtq9QERsA7YBrF27Nvo5WHO73rdVGF8zxcceO34XH3rf2OCDqVATB+M2a4KuWgFFxPMR8WpE/Bj4K147zTMJrGpZdCXwbG8hmplZGbpKAJKWt0z+JjDdQmgnsEHSCZJOB1YDD/UWopmZlaGTZqCfBb4GvFXSpKQPAP9V0mOSHgV+Dfh9gIjYB9wJfAu4D7g6Il4tLXqzEkhaJekBSfsl7ZN0TSo/RdL9kp5Mf09uWcfNn23ozHsNICKualN8yxzL3wDc0EtQZhWbomjY8LCknwL2Srof2ATsioitkrYAW4DrZjR/Pg34sqQz/OPH6s49gc1miIjDEfFwev4ysJ+iNdt6YHtabDtwZXru5s82lDwegNkcJI0C5wG7gZGIOAxFkpB0alqs8ubPZTR9blozZjdHPp4TgNksJJ0I3AVcGxEvSe1aOReLtikbaPPnMpo+N60Zs5sjH8+ngMzakLSY4p//HRFxdyp+froFXPp7JJW7+bMNJScAsxlU/NS/BdgfETe2zNoJbEzPNwL3tJS7+bMNHZ8CMjvehcD7gcckPZLK/gDYCtyZmkI/DbwHiubPkqabP0/h5s82JJwAzGaIiAdpf14f4JJZ1nHzZxs6PgVkZpYpJwAzs0w5AZiZZcoJwMwsU04AZmaZcgIwM8uUE4CZWaacAMzMMtXJgDC3Sjoi6fGWMg+MYWY25Do5ArgdWDejbAvFwBirgV1pmhkDY6wDbpK0qG/RmplZ38ybACLiq8ALM4o9MIaZ2ZDr9l5AtR0YA8oZHKMbTRtQo1seiMOsnvp9M7jKB8aAcgbH6EbTBtTolgfiMKunblsBeWAMM7Mh120C8MAYZmZDbt5TQJI+C4wByyRNAn+EB8YwMxt68yaAiLhqllkeGMPMbIi5J7CZWaacAMzMMuUxgSsw2kUz1UNbryghEjPLmY8AzMwy5QRgZpYpJwAzs0w5AZiZZcoJwKwNj4NhOah9K6BuWsyY9cHtwCeA/9FSNj0OxlZJW9L0dTPGwTgN+LKkM5raC96t2JrDRwBmbXgcDMtB7Y8AzGqktuNglDEGxmzjWXSjDuNBeFyK4zkBmPWu8nEwyhgDY7bxLLpRhzEwPC7F8XwKyKxzHgfDGsUJwKxzHgfDGsWngMza8DgYlgMngCHhpneD5XEwLAc9JQBJh4CXgVeBqYhYK+kU4G+AUeAQ8N6I+GFvYZqZWb/14xrAr0XEuRGxNk1Pd5ZZDexK02ZmVjNlXASerbOMmZnVSK/XAAL4kqQAPp3aOM/WWeZ1Ou0QU0YHl0HpZ0eabtSl04s74CyMb39ig9JrArgwIp5N/+Tvl/REpyt22iGmjA4ug9LPjjTdqEPnG3AHHLO66ukUUEQ8m/4eAT5Pcf+T2TrLmJlZjXSdACQtlfRT08+BXwceZ/bOMmZmViO9nJ8YAT4vafp1/joi7pP0Ddp0ljEzs3rpOgFExHeAc9qU/4BZOsuYmVl9uCdwg3XbmsQ9iK3f3JO9nnwzODOzTDkBmJllygnAzCxTTgBmZplyAjAzy5QTgJlZppwAzMwy5QRgZpYpdwQzs1pyR8by+QjAzCxTPgKwyvj2AGbVcgKwvpjrn/n4mqmhHtjHrKmcAOw4HpLQLA++BmBmlikfAZhZo8x2BDvXqchcry2VdgQgaZ2kA5IOStpS1vuY1YXrvA2bUo4AJC0CPglcCkwC35C0MyK+Vcb7mVXNdd46Ube+DWWdAjofOJiGjUTSDmA94C+DNZXr/BDLtUlyWQlgBfBMy/Qk8PbWBSRtBjanyaOSDpQUS2V+D5YB3686jqr1cz/oo3POfks/3qNL89Z5GK5637T62+/tmacu9tUc7/XWXl63rASgNmXxuomIbcC2kt6/FiTtiYi1VcdRtUz2w7x1Hoar3jftc2va9kCxTb2sX9ZF4ElgVcv0SuDZkt7LrA5c523olJUAvgGslnS6pDcCG4CdJb2XWR24ztvQKeUUUERMSfoQ8HfAIuDWiNhXxnvV3FAc6g9A4/dDQ+t80z63pm0P9LhNijjuNKWZmWXAt4IwM8uUE4CZWaacAHogaZWkByTtl7RP0jWp/BRJ90t6Mv09uWWd69OtAg5Iuqy66PtH0iJJ35R0b5rOavuHUVPrbtPqoqSTJH1O0hPps3pHP7fJCaA3U8B4RJwJXABcLeksYAuwKyJWA7vSNGneBuBsYB1wU7qFwLC7BtjfMp3b9g+jptbdptXFjwP3RcTbgHMotq1v2+QE0IOIOBwRD6fnL1N8OCsobgGwPS22HbgyPV8P7IiIVyLiKeAgxS0EhpaklcAVwM0txdls/7BqYt1tWl2U9CbgV4BbACLinyLiRfq4TU4AfSJpFDgP2A2MRMRhKL5owKlpsXa3C1gxuChL8RfAh4Eft5TltP1Dr0F1t2l18WeB7wG3pdNaN0taSh+3yQmgDySdCNwFXBsRL821aJuyoW2HK+ndwJGI2NvpKm3Khnb7m6ApdbehdfENwC8Cn4qI84BjpNM9s1jwNjkB9EjSYoov0B0RcXcqfl7S8jR/OXAklTftdgEXAr8h6RCwA7hY0mfIZ/uHWsPqbhPr4iQwGRG70/TnKBJC37bJCaAHkkRxfm5/RNzYMmsnsDE93wjc01K+QdIJkk4HVgN/JumHkk5oed0JSR+c8V5jkiZb31vShyQ9KukfJD2X1tvQ/y1tLyKuj4iVETFKcfHpKxHxOyxs+x8aVLz2mj7V3a4/O0kXSfo/kv5e0guS/rekX5K0SdKrko7OeJyW1jsk6Uep7HlJt0k6sYl1MSKeA56RNH3Hz0sobi/ev22KCD+6fAAXURxiPQo8kh7vAn6a4ur8k+nvKS3r/CHwbeBA+vBeBV4A3tOyzATwwRnvNUbxa2B6+r9TXOS5FFhCcfuBi4DbK9oXY8C96Xmn23951Z9hro8+1N2uPzvgTcCLwFWp3i4Bfh34BWAT8OAc6x4C3pmerwAeB7Y2tS4C5wJ70uf0BeDkfm6TbwVRIUn/CbiM4uLbGRHx7lQ+AXwmIm5uWXYsla2UdAZFq423R0RPt4M1GzRJa4EvR8RJbeZtovjxc9Es6x5K87+cpv8bcOb0d8cWxqeAqvW7wB3pcZmkkQ7Xuxh4xv/8bUj9P+BVSdslXd7akWkhJK2iOGr5Zl+jy4gTQEUkXUQxgtWdUbRc+Dbw2x2uvgx4bsbrTUp6UdI/SqpyZCyzOUXR2mj6FNRfAd+TtLPlB9AFqS5PP7494yW+IOlF4EHgfwJ/OrDgG8YJoDobgS9FxPQQdX/Naxd2poDFM5ZfDPxzev4DYHnrzIhYSZEYTqB9czCz2oiI/RGxKdXbnwdOo2jHD/D1iDip5fHvZqx+ZSp/S0T8x4j40UCDbxAngApIWgK8F/jV1HrnOeD3gXMknQM8DYzOWO104Lvp+VeAlelcqtlQi4gngNspEoENkBNANa6kaP1zFsVV/nOBM4H/RXFd4G+Afy/p/NTc8wyKBLEDICIOAJ8Gdki6VNKSdM+PXx78ppgtjKS3SRpPt26YPpd/FfD1aiPLjxNANTYCt0XE0xHx3PQD+ATwPl67wdNtwN8Df0txz4/W0X+uBv4SuJGiGekk8BHgtyiOIMzq6mXg7cBuScco/vE/Doyn+e9o0w/gl6oKtsncDNTMLFM+AjAzy5QTgJlZppwAzMwy5QRgZpapN1QdAMCyZctidHS06jAW7NixYyxdurTqMGqj6v2xd+/e70fEmysLYIGqqvdVf07zqXN8dYut1zpfiwQwOjrKnj3Dd1ubiYkJxsbGqg6jNqreH5K+O/9S9VFVva/6c5pPneOrW2y91nmfAjIzy5QTgJlZppwAzMwyVYtrAHUwuuWLC15nfM0UY/0PxWzBFlJ/x9dMsSktf2jrFWWFZEPARwBmZplyAjAzy5QTgJlZppwAzMwy5QRgZpapeROApFWSHpC0X9I+Sdek8lMk3S/pyfT35JZ1rpd0UNIBSZeVuQFmZtadTo4ApoDxiDgTuAC4WtJZFCNW7YqI1bw2ghVp3gbgbGAdcFMartDMzGpk3gQQEYcj4uH0/GVgP7ACWE8xTCHp75Xp+XpgR0S8EhFPAQeB8/sduJmZ9WZBHcEkjQLnAbuBkYg4DEWSkHRqWmwFrx/ceTKVzXytzcBmgJGRESYmJhYYen+Nr5la8DojS6g87jo5evSo94fZEOk4AUg6EbgLuDYiXpI066Jtyo4beDgitpEGOV+7dm1UfYe9TV32BP7wfccWvF5Te1/W7U6JZja3jloBSVpM8c//joi4OxU/L2l5mr8cOJLKJ4FVLauvBJ7tT7hmZtYvnbQCEnALsD8ibmyZtRPYmJ5vBO5pKd8g6QRJpwOrgYf6F7JZuST9pKSHJP3f1PLtP6dyt3yzRunkCOBC4P3AxZIeSY93AVuBSyU9CVyapomIfcCdwLeA+4CrI+LVUqI3K8crwMURcQ5wLrBO0gW45Zs1zLzXACLiQdqf1we4ZJZ1bgBu6CEus8pERABH0+Ti9AiKFm5jqXw7MAFcR0vLN+ApSdMt3742uKjNFs63gzZrI/2C3wv8HPDJiNgtqaeWb+l1S2n9tpBWbCNLXlu+jq226tyarM6xdcMJwKyNdNryXEknAZ+X9PNzLN5Ry7f0uqW0fltIK7bxNVN87LHiq3/off15/36qc2uyOsfWDd8LyGwOEfEixamedbjlmzWME4DZDJLenH75I2kJ8E7gCdzyzRrGp4DMjrcc2J6uA/wEcGdE3Cvpa8Cdkj4APA28B4qWb5KmW75N4ZZvNiScAMxmiIhHKW55MrP8B7jlmzWITwGZmWXKCcDMLFONPAU02sWN3czMcuMjADOzTDkBmJllygnAzCxTTgBmZplyAjAzy5QTgJlZppwAzMwy1ch+AGbWmW76zBzaekUJkVgVfARgZpYpJwAzs0w5AZiZZcoJwMwsU04AZmaZcgIwm0HSKkkPSNovaZ+ka1L5KZLul/Rk+ntyyzrXSzoo6YCky6qL3qxzTgBmx5sCxiPiTOAC4GpJZwFbgF0RsRrYlaZJ8zYAZ1MMHn9TGk7SrNacAMxmiIjDEfFwev4ysB9YAawHtqfFtgNXpufrgR0R8UpEPAUcBM4fbNRmC+eOYGZzkDRKMT7wbmAkIg5DkSQknZoWWwF8vWW1yVTW7vU2A5sBRkZGmJiY6Euc42umOl52ZMnClp+pXzHP5ujRo6W/R7fqHFs3nADMZiHpROAu4NqIeEnSrIu2KYt2C0bENmAbwNq1a2NsbKwPkcKmBfToHV8zxcce6/6rf+h9Y12v24mJiQn6tV/6rc6xdcOngMzakLSY4p//HRFxdyp+XtLyNH85cCSVTwKrWlZfCTw7qFjNuuUEYDaDip/6twD7I+LGllk7gY3p+UbgnpbyDZJOkHQ6sBp4aFDxmnXLp4DMjnch8H7gMUmPpLI/ALYCd0r6APA08B6AiNgn6U7gWxQtiK6OiFcHH7bZwjgBmM0QEQ/S/rw+wCWzrHMDcENpQZmVYN5TQJJulXRE0uMtZe4QY2Y25Dq5BnA7ReeWVu4QY2Y25OZNABHxVeCFGcXuEGNmNuS6vQZQ2w4x0Fsnl4XotkNNkzqStGpaJxmzpuv3ReDKO8TAwjrF9KLbDjVld6SpStM6yZg1Xbf9ANwhxsxsyHWbANwhxsxsyM17/kLSZ4ExYJmkSeCPcIcYM7OhN28CiIirZpnlDjFdGu3iGsWhrVeUEImZ5cz3AjIzy5QTgJlZppwAzMwy5QRgZpYpJwAzs0w5AZiZZcrjAZjZgrgZc3P4CMCsDY+DYTlwAjBr73Y8DoY1nBOAWRseB8Ny4GsAZp2r7TgYCxmXottxLHqxkO2s87gSdY6tG04AZr2rfByMhYyB0e04Fr1YyBgYdR5Xos6xdaP2CaCbFgdmJXle0vL069/jYNjQ8zUAs855HAxrlNofAVjBba8Hy+NgWA6cAMza8DgYlgOfAjIzy5QTgJlZppwAzMwy5WsAZjXjps82KD4CMDPLlBOAmVmmnADMzDLlBGBmliknADOzTDkBmJllys1AG6zb5oS+h5BZHnwEYGaWKScAM7NMOQGYmWXK1wDsOB57wPptIXVqfM0Um7Z80XVqAJwAzKyW3IihfKUlAEnrgI8Di4CbI2JrWe9l1Rvd8sV//eXWqaZ9UV3nbdiUcg1A0iLgk8DlwFnAVZLOKuO9zOrAdd6GUVlHAOcDByPiOwCSdgDrKcZMNQMad63Bdb4myryddutRbo3rYsfKSgArgGdapieBt7cuIGkzsDlNHpV0oKRYSvN7sAz4ftVx1MUg9oc+Oufst5T53vOYt85DPep93ettneNrjW2eujgoPdX5shKA2pTF6yYitgHbSnr/gZC0JyLWVh1HXWS+P+at81CPel/3z6nO8dU5tm6U1Q9gEljVMr0SeLak9zKrA9d5GzplJYBvAKslnS7pjcAGYGdJ72VWB67zNnRKOQUUEVOSPgT8HUWTuFsjYl8Z71WxoT6FVYJs98eQ1fm6f051jq/OsS2YIo47TWlmZhnwvYDMzDLlBGBmlikngDlIWiXpAUn7Je2TdE0qP0XS/ZKeTH9PblnnekkHJR2QdFl10ZdD0iJJ35R0b5rOdl/U0TDU2brXIUknSfqcpCfSfnxH3WLsFyeAuU0B4xFxJnABcHXq3r8F2BURq4FdaZo0bwNwNrAOuCndIqBJrgH2t0znvC/qaBjqbN3r0MeB+yLibcA5Kda6xdgXTgBziIjDEfFwev4yRUVYQdHFf3tabDtwZXq+HtgREa9ExFPAQYpbBDSCpJXAFcDNLcVZ7ou6qnudrXsdkvQm4FeAWwAi4p8i4sU6xdhPTgAdkjQKnAfsBkYi4jAUXzjg1LRYu9sBrBhclKX7C+DDwI9bynLdF7VX0zpb9zr0s8D3gNvSaaqbJS2tWYx94wTQAUknAncB10bES3Mt2qasEe1sJb0bOBIReztdpU1ZI/bFMKhjnR2SOvQG4BeBT0XEecAx0umeWQx1PXcCmIekxRRfpDsi4u5U/Lyk5Wn+cuBIKm/y7QAuBH5D0iFgB3CxpM+Q576otRrX2WGoQ5PAZETsTtOfo0gIdYqxb5wA5iBJFOcC90fEjS2zdgIb0/ONwD0t5RsknSDpdGA18NCg4i1TRFwfESsjYpTiotdXIuJ3yHBf1Fmd6+ww1KGIeA54RtJbU9ElFLf0rk2MfRURfszyAC6iOJx7FHgkPd4F/DRFS4An099TWtb5Q+DbwAHg8qq3oaT9Mgbcm55nvS/q9hiWOlvnOgScC+xJ+/ALwMl1i7FfD98KwswsUz4FZGaWKScAM7NMOQGYmWXKCcDMLFNOAGZmmXICMDPLlBOAmVmm/gWjhSzU6SNwDAAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"#Data might be widely distributed so let's scale it between 0 and 1\nfrom sklearn import preprocessing\nminmax = preprocessing.MinMaxScaler(feature_range=(0,1))\nminmax.fit(x).transform(x)","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.696214Z","iopub.execute_input":"2021-08-08T04:28:48.696625Z","iopub.status.idle":"2021-08-08T04:28:48.713123Z","shell.execute_reply.started":"2021-08-08T04:28:48.696584Z","shell.execute_reply":"2021-08-08T04:28:48.711595Z"},"trusted":true},"execution_count":60,"outputs":[{"execution_count":60,"output_type":"execute_result","data":{"text/plain":"array([[0.58333333, 0.58275058, 0.        , ..., 0.24197396, 0.21689401,\n        0.27370822],\n       [0.58333333, 0.58275058, 0.1       , ..., 0.15328811, 0.13151274,\n        0.13721348],\n       [0.58333333, 0.58275058, 0.2       , ..., 0.18479046, 0.286268  ,\n        0.0551343 ],\n       ...,\n       [1.        , 1.        , 0.8       , ..., 0.33582449, 0.66894695,\n        0.68216256],\n       [1.        , 1.        , 0.9       , ..., 0.31348069, 0.43433203,\n        0.58096062],\n       [1.        , 1.        , 1.        , ..., 0.22622825, 0.37333569,\n        0.61803626]])"},"metadata":{}}]},{"cell_type":"code","source":"#Let's divide the dataset into 2 sets:train and test in ratio (4:1)\nfrom sklearn import model_selection,neighbors\nfrom sklearn.model_selection import train_test_split\n\nx_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)\nprint('done')","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.715009Z","iopub.execute_input":"2021-08-08T04:28:48.715489Z","iopub.status.idle":"2021-08-08T04:28:48.725481Z","shell.execute_reply.started":"2021-08-08T04:28:48.715441Z","shell.execute_reply":"2021-08-08T04:28:48.724349Z"},"trusted":true},"execution_count":61,"outputs":[{"name":"stdout","text":"done\n","output_type":"stream"}]},{"cell_type":"code","source":"#Let's see how our train set looks like\nx_train.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.730421Z","iopub.execute_input":"2021-08-08T04:28:48.731108Z","iopub.status.idle":"2021-08-08T04:28:48.757578Z","shell.execute_reply.started":"2021-08-08T04:28:48.731057Z","shell.execute_reply":"2021-08-08T04:28:48.756212Z"},"trusted":true},"execution_count":62,"outputs":[{"execution_count":62,"output_type":"execute_result","data":{"text/plain":"     STATE  DISTRICT  YEAR     JAN     FEB     MAR     APR     MAY     JUN  \\\n285    104    104002  2010  132.35  147.80  189.22  282.07  204.16  281.77   \n75     109    109001  2009   86.13   35.56  237.37  183.95  239.64   54.51   \n755    113    113007  2007  665.20  476.31  229.65  290.45  171.51  287.15   \n617    112    112010  2001  248.31  166.11  304.94  213.58  119.12  334.54   \n313    101    101002  2005   51.45   93.53  163.38  171.01  218.36   68.27   \n\n        JUL     AUG     SEP     OCT      0V     DEC  ANNUAL RAINFALL  \n285  184.29  205.93  183.01  121.62  259.54  220.18          2411.96  \n75   177.39  294.59  205.61  215.24  474.45  100.80          2305.25  \n755  354.60  266.08  358.46  257.24  371.74  526.44          4254.84  \n617  130.77  152.89  390.77  667.85  275.17  278.29          3282.32  \n313  234.98  307.62  132.12  268.09  247.55  205.03          2161.37  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>STATE</th>\n      <th>DISTRICT</th>\n      <th>YEAR</th>\n      <th>JAN</th>\n      <th>FEB</th>\n      <th>MAR</th>\n      <th>APR</th>\n      <th>MAY</th>\n      <th>JUN</th>\n      <th>JUL</th>\n      <th>AUG</th>\n      <th>SEP</th>\n      <th>OCT</th>\n      <th>0V</th>\n      <th>DEC</th>\n      <th>ANNUAL RAINFALL</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>285</th>\n      <td>104</td>\n      <td>104002</td>\n      <td>2010</td>\n      <td>132.35</td>\n      <td>147.80</td>\n      <td>189.22</td>\n      <td>282.07</td>\n      <td>204.16</td>\n      <td>281.77</td>\n      <td>184.29</td>\n      <td>205.93</td>\n      <td>183.01</td>\n      <td>121.62</td>\n      <td>259.54</td>\n      <td>220.18</td>\n      <td>2411.96</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>109</td>\n      <td>109001</td>\n      <td>2009</td>\n      <td>86.13</td>\n      <td>35.56</td>\n      <td>237.37</td>\n      <td>183.95</td>\n      <td>239.64</td>\n      <td>54.51</td>\n      <td>177.39</td>\n      <td>294.59</td>\n      <td>205.61</td>\n      <td>215.24</td>\n      <td>474.45</td>\n      <td>100.80</td>\n      <td>2305.25</td>\n    </tr>\n    <tr>\n      <th>755</th>\n      <td>113</td>\n      <td>113007</td>\n      <td>2007</td>\n      <td>665.20</td>\n      <td>476.31</td>\n      <td>229.65</td>\n      <td>290.45</td>\n      <td>171.51</td>\n      <td>287.15</td>\n      <td>354.60</td>\n      <td>266.08</td>\n      <td>358.46</td>\n      <td>257.24</td>\n      <td>371.74</td>\n      <td>526.44</td>\n      <td>4254.84</td>\n    </tr>\n    <tr>\n      <th>617</th>\n      <td>112</td>\n      <td>112010</td>\n      <td>2001</td>\n      <td>248.31</td>\n      <td>166.11</td>\n      <td>304.94</td>\n      <td>213.58</td>\n      <td>119.12</td>\n      <td>334.54</td>\n      <td>130.77</td>\n      <td>152.89</td>\n      <td>390.77</td>\n      <td>667.85</td>\n      <td>275.17</td>\n      <td>278.29</td>\n      <td>3282.32</td>\n    </tr>\n    <tr>\n      <th>313</th>\n      <td>101</td>\n      <td>101002</td>\n      <td>2005</td>\n      <td>51.45</td>\n      <td>93.53</td>\n      <td>163.38</td>\n      <td>171.01</td>\n      <td>218.36</td>\n      <td>68.27</td>\n      <td>234.98</td>\n      <td>307.62</td>\n      <td>132.12</td>\n      <td>268.09</td>\n      <td>247.55</td>\n      <td>205.03</td>\n      <td>2161.37</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"y_train.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.761292Z","iopub.execute_input":"2021-08-08T04:28:48.761921Z","iopub.status.idle":"2021-08-08T04:28:48.771513Z","shell.execute_reply.started":"2021-08-08T04:28:48.761879Z","shell.execute_reply":"2021-08-08T04:28:48.770204Z"},"trusted":true},"execution_count":63,"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"285    1\n75     1\n755    0\n617    0\n313    0\nName: FLOOD , dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"# Prediction Algorithms:","metadata":{}},{"cell_type":"markdown","source":"# 1. KNN Classifier","metadata":{}},{"cell_type":"code","source":"clf = neighbors.KNeighborsClassifier()\nknn_clf = clf.fit(x_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.773293Z","iopub.execute_input":"2021-08-08T04:28:48.774002Z","iopub.status.idle":"2021-08-08T04:28:48.784412Z","shell.execute_reply.started":"2021-08-08T04:28:48.773908Z","shell.execute_reply":"2021-08-08T04:28:48.783427Z"},"trusted":true},"execution_count":64,"outputs":[]},{"cell_type":"code","source":"#Let's predict chances of flood\ny_predict = knn_clf.predict(x_test)\nprint('predicted chances of flood')\nprint(y_predict)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2021-08-08T04:28:48.786186Z","iopub.execute_input":"2021-08-08T04:28:48.786818Z","iopub.status.idle":"2021-08-08T04:28:48.806813Z","shell.execute_reply.started":"2021-08-08T04:28:48.786775Z","shell.execute_reply":"2021-08-08T04:28:48.805888Z"},"trusted":true},"execution_count":65,"outputs":[{"name":"stdout","text":"predicted chances of flood\n[1 1 0 0 0 1 0 1 1 1 0 1 0 0 0 0 0 0 1 1 0 1 1 0 0 0 1 0 1 1 0 0 1 1 0 1 0\n 0 0 1 0 1 0 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 0 1 1 0 0 1 1 0 1\n 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 0 0 1 0 0 1 0\n 1 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 1 0 1\n 0 0 1 0 0 0 0 1 0 0 1 0 0 1 1 0 0]\n","output_type":"stream"}]},{"cell_type":"code","source":"#Actual chances of flood\nprint(\"actual values of floods:\")\nprint(y_test)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2021-08-08T04:28:48.808889Z","iopub.execute_input":"2021-08-08T04:28:48.809336Z","iopub.status.idle":"2021-08-08T04:28:48.817944Z","shell.execute_reply.started":"2021-08-08T04:28:48.809293Z","shell.execute_reply":"2021-08-08T04:28:48.816712Z"},"trusted":true},"execution_count":66,"outputs":[{"name":"stdout","text":"actual values of floods:\n549    1\n392    0\n646    0\n750    0\n411    0\n      ..\n391    0\n474    0\n680    0\n794    0\n595    0\nName: FLOOD , Length: 165, dtype: int64\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.model_selection import cross_val_score","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.819882Z","iopub.execute_input":"2021-08-08T04:28:48.820691Z","iopub.status.idle":"2021-08-08T04:28:48.827537Z","shell.execute_reply.started":"2021-08-08T04:28:48.820641Z","shell.execute_reply":"2021-08-08T04:28:48.826084Z"},"trusted":true},"execution_count":67,"outputs":[]},{"cell_type":"code","source":"knn_accuracy = cross_val_score(knn_clf,x_test,y_test,cv=3,scoring='accuracy',n_jobs=-1)","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.829521Z","iopub.execute_input":"2021-08-08T04:28:48.830279Z","iopub.status.idle":"2021-08-08T04:28:48.898302Z","shell.execute_reply.started":"2021-08-08T04:28:48.830232Z","shell.execute_reply":"2021-08-08T04:28:48.897070Z"},"trusted":true},"execution_count":68,"outputs":[]},{"cell_type":"code","source":"knn_accuracy.mean()","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.899614Z","iopub.execute_input":"2021-08-08T04:28:48.899954Z","iopub.status.idle":"2021-08-08T04:28:48.909697Z","shell.execute_reply.started":"2021-08-08T04:28:48.899908Z","shell.execute_reply":"2021-08-08T04:28:48.908551Z"},"trusted":true},"execution_count":69,"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"0.6121212121212122"},"metadata":{}}]},{"cell_type":"markdown","source":"# 2. Logistic Regression","metadata":{}},{"cell_type":"code","source":"x_train_std = minmax.fit_transform(x_train)\nx_test_std = minmax.transform(x_test)","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.911728Z","iopub.execute_input":"2021-08-08T04:28:48.912508Z","iopub.status.idle":"2021-08-08T04:28:48.936894Z","shell.execute_reply.started":"2021-08-08T04:28:48.912463Z","shell.execute_reply":"2021-08-08T04:28:48.932861Z"},"trusted":true},"execution_count":70,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import cross_val_score\nfrom sklearn.linear_model import LogisticRegression\n\nlr = LogisticRegression()\nlr_clf = lr.fit(x_train_std,y_train)\n\nlr_accuracy = cross_val_score(lr_clf,x_test_std,y_test,cv=3,scoring='accuracy',n_jobs=-1)","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:48.938322Z","iopub.execute_input":"2021-08-08T04:28:48.938702Z","iopub.status.idle":"2021-08-08T04:28:49.017547Z","shell.execute_reply.started":"2021-08-08T04:28:48.938663Z","shell.execute_reply":"2021-08-08T04:28:49.015999Z"},"trusted":true},"execution_count":71,"outputs":[]},{"cell_type":"code","source":"lr_accuracy.mean()","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:49.023615Z","iopub.execute_input":"2021-08-08T04:28:49.025486Z","iopub.status.idle":"2021-08-08T04:28:49.047779Z","shell.execute_reply.started":"2021-08-08T04:28:49.025349Z","shell.execute_reply":"2021-08-08T04:28:49.046293Z"},"trusted":true},"execution_count":72,"outputs":[{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"0.6424242424242425"},"metadata":{}}]},{"cell_type":"code","source":"y_predict = lr_clf.predict(x_test_std)\nprint('Predicted chances of flood')\n##print('Predicted No. Occurence of flood')\nprint(y_predict)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2021-08-08T04:28:49.049601Z","iopub.execute_input":"2021-08-08T04:28:49.053176Z","iopub.status.idle":"2021-08-08T04:28:49.065531Z","shell.execute_reply.started":"2021-08-08T04:28:49.053119Z","shell.execute_reply":"2021-08-08T04:28:49.063865Z"},"trusted":true},"execution_count":73,"outputs":[{"name":"stdout","text":"Predicted chances of flood\n[1 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 1 0 0 0\n 0 0 1 0 1 1 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 1\n 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 0 0\n 1 0 0 1 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n 0 1 1 0 0 0 0 1 1 0 0 1 0 0 1 0 0]\n","output_type":"stream"}]},{"cell_type":"code","source":"print('Actual chances of flood')\n##print('Actual No. Occurence of flood')\nprint(y_test.values)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2021-08-08T04:28:49.067321Z","iopub.execute_input":"2021-08-08T04:28:49.067829Z","iopub.status.idle":"2021-08-08T04:28:49.082677Z","shell.execute_reply.started":"2021-08-08T04:28:49.067786Z","shell.execute_reply":"2021-08-08T04:28:49.078134Z"},"trusted":true},"execution_count":74,"outputs":[{"name":"stdout","text":"Actual chances of flood\n[1 0 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 1 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 0 0 0\n 0 0 1 1 0 0 1 0 1 0 1 1 0 0 0 1 1 0 1 0 1 1 0 0 1 0 0 0 1 0 1 1 0 1 1 0 1\n 1 1 1 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 0 1 1 1 1 0 0 1 1 1 1 1 0\n 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 1 1 0 0 1 0 1 1 1\n 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 0 0]\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import accuracy_score,recall_score,roc_auc_score,confusion_matrix\nprint(\"\\naccuracy score: %f\"%(accuracy_score(y_test,y_predict)*100))\nprint(\"recall score: %f\"%(recall_score(y_test,y_predict)*100))\nprint(\"roc score: %f\"%(roc_auc_score(y_test,y_predict)*100))","metadata":{"tags":[],"execution":{"iopub.status.busy":"2021-08-08T04:28:49.084135Z","iopub.execute_input":"2021-08-08T04:28:49.084889Z","iopub.status.idle":"2021-08-08T04:28:49.102485Z","shell.execute_reply.started":"2021-08-08T04:28:49.084823Z","shell.execute_reply":"2021-08-08T04:28:49.098603Z"},"trusted":true},"execution_count":75,"outputs":[{"name":"stdout","text":"\naccuracy score: 60.000000\nrecall score: 35.820896\nroc score: 56.175754\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# 3. Decision tree classification","metadata":{}},{"cell_type":"code","source":"from sklearn.tree import DecisionTreeClassifier\ndtc_clf = DecisionTreeClassifier()\ndtc_clf.fit(x_train,y_train)\ndtc_clf_acc = cross_val_score(dtc_clf,x_train_std,y_train,cv=3,scoring=\"accuracy\",n_jobs=-1)\ndtc_clf_acc","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:49.103986Z","iopub.execute_input":"2021-08-08T04:28:49.104547Z","iopub.status.idle":"2021-08-08T04:28:49.176679Z","shell.execute_reply.started":"2021-08-08T04:28:49.104505Z","shell.execute_reply":"2021-08-08T04:28:49.175690Z"},"trusted":true},"execution_count":76,"outputs":[{"execution_count":76,"output_type":"execute_result","data":{"text/plain":"array([0.64545455, 0.69545455, 0.67272727])"},"metadata":{}}]},{"cell_type":"code","source":"#Predicted flood chances\ny_pred = dtc_clf.predict(x_test)\nprint(y_pred)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2021-08-08T04:28:49.178973Z","iopub.execute_input":"2021-08-08T04:28:49.181291Z","iopub.status.idle":"2021-08-08T04:28:49.198705Z","shell.execute_reply.started":"2021-08-08T04:28:49.181250Z","shell.execute_reply":"2021-08-08T04:28:49.197623Z"},"trusted":true},"execution_count":77,"outputs":[{"name":"stdout","text":"[1 1 0 0 1 1 0 1 1 1 0 1 0 1 1 0 0 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 1 0 0 0\n 0 0 1 0 1 0 1 0 1 0 1 1 0 0 0 0 1 0 1 0 1 1 0 0 1 1 0 0 1 0 1 1 0 1 1 1 1\n 0 1 1 1 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 1 1 1 0 1 0 1 1 1 0 0 0 0 0 1 0 1 0\n 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0\n 0 0 1 0 1 0 1 1 1 1 0 1 0 1 1 0 0]\n","output_type":"stream"}]},{"cell_type":"code","source":"#Actual flood chances\nprint(\"actual values:\")\nprint(y_test.values)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2021-08-08T04:28:49.202198Z","iopub.execute_input":"2021-08-08T04:28:49.203854Z","iopub.status.idle":"2021-08-08T04:28:49.215933Z","shell.execute_reply.started":"2021-08-08T04:28:49.203808Z","shell.execute_reply":"2021-08-08T04:28:49.214100Z"},"trusted":true},"execution_count":78,"outputs":[{"name":"stdout","text":"actual values:\n[1 0 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 1 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 0 0 0\n 0 0 1 1 0 0 1 0 1 0 1 1 0 0 0 1 1 0 1 0 1 1 0 0 1 0 0 0 1 0 1 1 0 1 1 0 1\n 1 1 1 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 0 1 1 1 1 0 0 1 1 1 1 1 0\n 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 1 1 0 0 1 0 1 1 1\n 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 0 0]\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import accuracy_score,recall_score,roc_auc_score,confusion_matrix\nprint(\"\\naccuracy score:%f\"%(accuracy_score(y_test,y_pred)*100))\nprint(\"recall score:%f\"%(recall_score(y_test,y_pred)*100))\nprint(\"roc score:%f\"%(roc_auc_score(y_test,y_pred)*100))","metadata":{"tags":[],"execution":{"iopub.status.busy":"2021-08-08T04:28:49.218671Z","iopub.execute_input":"2021-08-08T04:28:49.220772Z","iopub.status.idle":"2021-08-08T04:28:49.245168Z","shell.execute_reply.started":"2021-08-08T04:28:49.220724Z","shell.execute_reply":"2021-08-08T04:28:49.244093Z"},"trusted":true},"execution_count":79,"outputs":[{"name":"stdout","text":"\naccuracy score:73.333333\nrecall score:76.119403\nroc score:73.773987\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# 4. Random Forest Classification","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\nrmf = RandomForestClassifier(max_depth=3,random_state=0)\nrmf_clf = rmf.fit(x_train,y_train)\nrmf_clf","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:49.248913Z","iopub.execute_input":"2021-08-08T04:28:49.252665Z","iopub.status.idle":"2021-08-08T04:28:49.607831Z","shell.execute_reply.started":"2021-08-08T04:28:49.252607Z","shell.execute_reply":"2021-08-08T04:28:49.607123Z"},"trusted":true},"execution_count":80,"outputs":[{"execution_count":80,"output_type":"execute_result","data":{"text/plain":"RandomForestClassifier(max_depth=3, random_state=0)"},"metadata":{}}]},{"cell_type":"code","source":"rmf_clf_acc = cross_val_score(rmf_clf,x_train_std,y_train,cv=3,scoring=\"accuracy\",n_jobs=-1)\n#rmf_proba = cross_val_predict(rmf_clf,x_train_std,y_train,cv=3,method='predict_proba')","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:49.609816Z","iopub.execute_input":"2021-08-08T04:28:49.610391Z","iopub.status.idle":"2021-08-08T04:28:50.154903Z","shell.execute_reply.started":"2021-08-08T04:28:49.610351Z","shell.execute_reply":"2021-08-08T04:28:50.154158Z"},"trusted":true},"execution_count":81,"outputs":[]},{"cell_type":"code","source":"rmf_clf_acc","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:50.156073Z","iopub.execute_input":"2021-08-08T04:28:50.156437Z","iopub.status.idle":"2021-08-08T04:28:50.167293Z","shell.execute_reply.started":"2021-08-08T04:28:50.156399Z","shell.execute_reply":"2021-08-08T04:28:50.165869Z"},"trusted":true},"execution_count":82,"outputs":[{"execution_count":82,"output_type":"execute_result","data":{"text/plain":"array([0.61363636, 0.64090909, 0.62272727])"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = rmf_clf.predict(x_test)","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:50.168926Z","iopub.execute_input":"2021-08-08T04:28:50.169471Z","iopub.status.idle":"2021-08-08T04:28:50.191332Z","shell.execute_reply.started":"2021-08-08T04:28:50.169428Z","shell.execute_reply":"2021-08-08T04:28:50.190340Z"},"trusted":true},"execution_count":83,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import accuracy_score,recall_score,roc_auc_score,confusion_matrix\nprint(\"\\naccuracy score:%f\"%(accuracy_score(y_test,y_pred)*100))\nprint(\"recall score:%f\"%(recall_score(y_test,y_pred)*100))\nprint(\"roc score:%f\"%(roc_auc_score(y_test,y_pred)*100))","metadata":{"tags":[],"execution":{"iopub.status.busy":"2021-08-08T04:28:50.192802Z","iopub.execute_input":"2021-08-08T04:28:50.193229Z","iopub.status.idle":"2021-08-08T04:28:50.207143Z","shell.execute_reply.started":"2021-08-08T04:28:50.193186Z","shell.execute_reply":"2021-08-08T04:28:50.205880Z"},"trusted":true},"execution_count":84,"outputs":[{"name":"stdout","text":"\naccuracy score:67.878788\nrecall score:62.686567\nroc score:67.057569\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# 5. Enseble Learning","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import VotingClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\n\nlog_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\nrnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\nknn_clf = KNeighborsClassifier()\n\nvoting = VotingClassifier(\n    estimators=[('lr', log_clf), ('rf', rnd_clf), ('knn', knn_clf)],\n    voting='hard')","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:50.208419Z","iopub.execute_input":"2021-08-08T04:28:50.208664Z","iopub.status.idle":"2021-08-08T04:28:50.217406Z","shell.execute_reply.started":"2021-08-08T04:28:50.208639Z","shell.execute_reply":"2021-08-08T04:28:50.216658Z"},"trusted":true},"execution_count":85,"outputs":[]},{"cell_type":"code","source":"voting_clf = voting.fit(x_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:50.221943Z","iopub.execute_input":"2021-08-08T04:28:50.222212Z","iopub.status.idle":"2021-08-08T04:28:50.270054Z","shell.execute_reply.started":"2021-08-08T04:28:50.222187Z","shell.execute_reply":"2021-08-08T04:28:50.269245Z"},"trusted":true},"execution_count":86,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import accuracy_score\n\nfor clf in (log_clf, rnd_clf, knn_clf, voting_clf):\n    clf.fit(x_train, y_train)\n    y_pred = clf.predict(x_test)\n    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))","metadata":{"tags":[],"execution":{"iopub.status.busy":"2021-08-08T04:28:50.272055Z","iopub.execute_input":"2021-08-08T04:28:50.272444Z","iopub.status.idle":"2021-08-08T04:28:50.400585Z","shell.execute_reply.started":"2021-08-08T04:28:50.272403Z","shell.execute_reply":"2021-08-08T04:28:50.399734Z"},"trusted":true},"execution_count":87,"outputs":[{"name":"stdout","text":"LogisticRegression 0.5454545454545454\nRandomForestClassifier 0.703030303030303\nKNeighborsClassifier 0.6606060606060606\nVotingClassifier 0.6545454545454545\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Comparing all the prediction models","metadata":{}},{"cell_type":"code","source":"models = []\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import VotingClassifier\nmodels.append(('KNN', KNeighborsClassifier()))\nmodels.append(('LR', LogisticRegression()))\nmodels.append(('DT', DecisionTreeClassifier()))\nmodels.append(('RF', RandomForestClassifier()))\nmodels.append(('EL', VotingClassifier(\n    estimators=[('lr', log_clf), ('rf', rnd_clf), ('knn', knn_clf)],\n    voting='hard')))\n\n\nnames = []\nscores = []\nfor name, model in models:\n    model.fit(x_train, y_train)\n    y_pred = model.predict(x_test)\n    scores.append(accuracy_score(y_test, y_pred))\n    names.append(name)\ntr_split = pd.DataFrame({'Name': names, 'Score': scores})\nprint(tr_split)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2021-08-08T04:28:50.402512Z","iopub.execute_input":"2021-08-08T04:28:50.402935Z","iopub.status.idle":"2021-08-08T04:28:50.855164Z","shell.execute_reply.started":"2021-08-08T04:28:50.402897Z","shell.execute_reply":"2021-08-08T04:28:50.854106Z"},"trusted":true},"execution_count":88,"outputs":[{"name":"stdout","text":"  Name     Score\n0  KNN  0.660606\n1   LR  0.539394\n2   DT  0.727273\n3   RF  0.745455\n4   EL  0.654545\n","output_type":"stream"}]},{"cell_type":"code","source":"import seaborn as sns\naxis = sns.barplot(x = 'Name', y = 'Score', data =tr_split )\naxis.set(xlabel='Classifier', ylabel='Accuracy')\nfor p in axis.patches:\n    height = p.get_height()\n    axis.text(p.get_x() + p.get_width()/2, height + 0.005, '{:1.4f}'.format(height), ha=\"center\") \n    \nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-08T04:28:50.856801Z","iopub.execute_input":"2021-08-08T04:28:50.857230Z","iopub.status.idle":"2021-08-08T04:28:51.003379Z","shell.execute_reply.started":"2021-08-08T04:28:50.857190Z","shell.execute_reply":"2021-08-08T04:28:51.002209Z"},"trusted":true},"execution_count":89,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# As we can observe and Compare different Models and Choose the best One.","metadata":{}},{"cell_type":"markdown","source":"# Give an Upvote if you like this Notebook.","metadata":{}}]}