Weather Data Classification using Decision Tree Classifier form Sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weather Data Classification using scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:blue; font-style:bold\"><br>\n",
    "\n",
    "Importing the Necessary Libraries<br></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import the necessary libraries of the python for demostration of the Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the data of the weather from the csv file using read_csv function of pandas dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./daily_weather.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:blue; font-style:bold\">Daily Weather Data Description</p>\n",
    "<br>\n",
    "The file **daily_weather.csv** is a comma-separated file that contains weather data.  This data comes from a weather station located in San Diego, California.  The weather station is equipped with sensors that capture weather-related measurements such as air temperature, air pressure, and relative humidity.  Data was collected for a period of three years, from September 2011 to September 2014, to ensure that sufficient data for different seasons and weather conditions is captured.<br><br>\n",
    "Let's now check all the columns in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Know about various columns in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['number', 'air_pressure_9am', 'air_temp_9am', 'avg_wind_direction_9am',\n",
       "       'avg_wind_speed_9am', 'max_wind_direction_9am', 'max_wind_speed_9am',\n",
       "       'rain_accumulation_9am', 'rain_duration_9am', 'relative_humidity_9am',\n",
       "       'relative_humidity_3pm'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number</th>\n",
       "      <th>air_pressure_9am</th>\n",
       "      <th>air_temp_9am</th>\n",
       "      <th>avg_wind_direction_9am</th>\n",
       "      <th>avg_wind_speed_9am</th>\n",
       "      <th>max_wind_direction_9am</th>\n",
       "      <th>max_wind_speed_9am</th>\n",
       "      <th>rain_accumulation_9am</th>\n",
       "      <th>rain_duration_9am</th>\n",
       "      <th>relative_humidity_9am</th>\n",
       "      <th>relative_humidity_3pm</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>918.060000</td>\n",
       "      <td>74.822000</td>\n",
       "      <td>271.100000</td>\n",
       "      <td>2.080354</td>\n",
       "      <td>295.400000</td>\n",
       "      <td>2.863283</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>917.347688</td>\n",
       "      <td>71.403843</td>\n",
       "      <td>101.935179</td>\n",
       "      <td>2.443009</td>\n",
       "      <td>140.471548</td>\n",
       "      <td>3.533324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.328697</td>\n",
       "      <td>19.426597</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>923.040000</td>\n",
       "      <td>60.638000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>17.067852</td>\n",
       "      <td>63.700000</td>\n",
       "      <td>22.100967</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.900000</td>\n",
       "      <td>14.460000</td>\n",
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       "      <td>70.138895</td>\n",
       "      <td>198.832133</td>\n",
       "      <td>4.337363</td>\n",
       "      <td>211.203341</td>\n",
       "      <td>5.190045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.189102</td>\n",
       "      <td>12.742547</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>921.160000</td>\n",
       "      <td>44.294000</td>\n",
       "      <td>277.800000</td>\n",
       "      <td>1.856660</td>\n",
       "      <td>136.500000</td>\n",
       "      <td>2.863283</td>\n",
       "      <td>8.9</td>\n",
       "      <td>14730.0</td>\n",
       "      <td>92.410000</td>\n",
       "      <td>76.740000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number  air_pressure_9am  air_temp_9am  avg_wind_direction_9am  \\\n",
       "0       0        918.060000     74.822000              271.100000   \n",
       "1       1        917.347688     71.403843              101.935179   \n",
       "2       2        923.040000     60.638000               51.000000   \n",
       "3       3        920.502751     70.138895              198.832133   \n",
       "4       4        921.160000     44.294000              277.800000   \n",
       "\n",
       "   avg_wind_speed_9am  max_wind_direction_9am  max_wind_speed_9am  \\\n",
       "0            2.080354              295.400000            2.863283   \n",
       "1            2.443009              140.471548            3.533324   \n",
       "2           17.067852               63.700000           22.100967   \n",
       "3            4.337363              211.203341            5.190045   \n",
       "4            1.856660              136.500000            2.863283   \n",
       "\n",
       "   rain_accumulation_9am  rain_duration_9am  relative_humidity_9am  \\\n",
       "0                    0.0                0.0              42.420000   \n",
       "1                    0.0                0.0              24.328697   \n",
       "2                    0.0               20.0               8.900000   \n",
       "3                    0.0                0.0              12.189102   \n",
       "4                    8.9            14730.0              92.410000   \n",
       "\n",
       "   relative_humidity_3pm  \n",
       "0              36.160000  \n",
       "1              19.426597  \n",
       "2              14.460000  \n",
       "3              12.742547  \n",
       "4              76.740000  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking is there exists null values in the dataset or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>number</th>\n",
       "      <th>air_pressure_9am</th>\n",
       "      <th>air_temp_9am</th>\n",
       "      <th>avg_wind_direction_9am</th>\n",
       "      <th>avg_wind_speed_9am</th>\n",
       "      <th>max_wind_direction_9am</th>\n",
       "      <th>max_wind_speed_9am</th>\n",
       "      <th>rain_accumulation_9am</th>\n",
       "      <th>rain_duration_9am</th>\n",
       "      <th>relative_humidity_9am</th>\n",
       "      <th>relative_humidity_3pm</th>\n",
       "    </tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>169.200000</td>\n",
       "      <td>2.192201</td>\n",
       "      <td>196.800000</td>\n",
       "      <td>2.930391</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>58.820000</td>\n",
       "      <td>182.600000</td>\n",
       "      <td>15.613841</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.500000</td>\n",
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       "      <td>915.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>183.300000</td>\n",
       "      <td>4.719943</td>\n",
       "      <td>189.900000</td>\n",
       "      <td>5.346287</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.260000</td>\n",
       "      <td>46.500000</td>\n",
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       "      <th>262</th>\n",
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       "      <td>58.380598</td>\n",
       "      <td>47.737753</td>\n",
       "      <td>10.636273</td>\n",
       "      <td>67.145843</td>\n",
       "      <td>13.671423</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.990876</td>\n",
       "      <td>16.461685</td>\n",
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       "      <td>920.480000</td>\n",
       "      <td>62.600000</td>\n",
       "      <td>194.400000</td>\n",
       "      <td>2.751436</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.869906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.580000</td>\n",
       "      <td>54.030000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     number  air_pressure_9am  air_temp_9am  avg_wind_direction_9am  \\\n",
       "16       16        917.890000           NaN              169.200000   \n",
       "111     111        915.290000     58.820000              182.600000   \n",
       "177     177        915.900000           NaN              183.300000   \n",
       "262     262        923.596607     58.380598               47.737753   \n",
       "277     277        920.480000     62.600000              194.400000   \n",
       "\n",
       "     avg_wind_speed_9am  max_wind_direction_9am  max_wind_speed_9am  \\\n",
       "16             2.192201              196.800000            2.930391   \n",
       "111           15.613841              189.000000                 NaN   \n",
       "177            4.719943              189.900000            5.346287   \n",
       "262           10.636273               67.145843           13.671423   \n",
       "277            2.751436                     NaN            3.869906   \n",
       "\n",
       "     rain_accumulation_9am  rain_duration_9am  relative_humidity_9am  \\\n",
       "16                     0.0                0.0              48.990000   \n",
       "111                    0.0                0.0              21.500000   \n",
       "177                    0.0                0.0              29.260000   \n",
       "262                    0.0                NaN              17.990876   \n",
       "277                    0.0                0.0              52.580000   \n",
       "\n",
       "     relative_humidity_3pm  \n",
       "16               51.190000  \n",
       "111              29.690000  \n",
       "177              46.500000  \n",
       "262              16.461685  \n",
       "277              54.030000  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.isnull().any(axis=1)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:blue; font-style:bold\"><br>\n",
    "\n",
    "Data Cleaning Steps<br><br></p>\n",
    "\n",
    "We will not need to number for each row so we can clean it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Cleaning process --> As number column contains unique values which can not help us making any decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['number']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculatoing the amount of data or say number of rows in the dataset before removing the rows containg null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1095\n"
     ]
    }
   ],
   "source": [
    "before_rows = data.shape[0]\n",
    "print(before_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing the rows which contains the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculatoing the amount of data or say number of rows in the dataset after removing the rows containg null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1064\n"
     ]
    }
   ],
   "source": [
    "after_rows = data.shape[0]\n",
    "print(after_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate how many rows are deleted which contains the Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "before_rows - after_rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter the values which contains more than 24.99 relative humidity at 3pm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    1\n",
       "Name: high_humidity_label, dtype: int32"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data = data.copy()\n",
    "clean_data['high_humidity_label'] = (clean_data['relative_humidity_3pm'] >24.99) *1\n",
    "clean_data['high_humidity_label'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   high_humidity_label\n",
       "0                    1\n",
       "1                    0\n",
       "2                    0\n",
       "3                    0\n",
       "4                    1"
      ]
     },
     "execution_count": 143,
     "metadata": {},
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   ],
   "source": [
    "y = clean_data[['high_humidity_label']].copy()\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    36.160000\n",
       "1    19.426597\n",
       "2    14.460000\n",
       "3    12.742547\n",
       "4    76.740000\n",
       "Name: relative_humidity_3pm, dtype: float64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data['relative_humidity_3pm'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   high_humidity_label\n",
       "0                    1\n",
       "1                    0\n",
       "2                    0\n",
       "3                    0\n",
       "4                    1"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:blue; font-style:bold\"><br>\n",
    "\n",
    "Use 9am Sensor Signals as Features to Predict Humidity at 3pm\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Storing all the Morning features other than Humidity at 3 pm in the morning feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "morning_features = ['air_pressure_9am', 'air_temp_9am', 'avg_wind_direction_9am',\n",
    "       'avg_wind_speed_9am', 'max_wind_direction_9am', 'max_wind_speed_9am',\n",
    "       'rain_accumulation_9am', 'rain_duration_9am', 'relative_humidity_9am']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copying the values from the clean_data dataset to new dataset x which only consist of the Morning Feature Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['air_pressure_9am', 'air_temp_9am', 'avg_wind_direction_9am',\n",
       "       'avg_wind_speed_9am', 'max_wind_direction_9am', 'max_wind_speed_9am',\n",
       "       'rain_accumulation_9am', 'rain_duration_9am', 'relative_humidity_9am'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=clean_data[morning_features].copy()\n",
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['high_humidity_label'], dtype='object')"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:blue; font-style:bold\"><br>\n",
    "\n",
    "Perform Test and Train split\n",
    "\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using train_test_split we have split the data into traing dataset and testing datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(x,y,test_size=0.33,random_state=324)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:blue; font-style:bold\"><br>\n",
    "\n",
    "Fit on Train Set\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have made a classifier for making the Decision Tree and to train the data using this classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=10,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humidity_classifier = DecisionTreeClassifier(max_leaf_nodes=10,random_state=0)\n",
    "humidity_classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.tree.tree.DecisionTreeClassifier"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(humidity_classifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:blue; font-style:bold\"><br>\n",
    "\n",
    "Predict on Test Set \n",
    "\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using humidity_classifier we have predicted the value for the X_test and stored it to y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = humidity_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 1, 1, 1, 0, 1, 1])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "456     0\n",
       "845     0\n",
       "693     1\n",
       "259     1\n",
       "723     1\n",
       "224     1\n",
       "300     1\n",
       "442     0\n",
       "585     1\n",
       "1057    1\n",
       "Name: high_humidity_label, dtype: int32"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test['high_humidity_label'][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:blue; font-style:bold\"><br>\n",
    "\n",
    "Measure Accuracy of the Classifier\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking our accuracy of the model using accuracy_score function from sklearn metrics which in this case is with around 90% accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.05681818181817"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_predicted)*100"
   ]
  }
 ],
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