Weather Forecasting.ipynb

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      "            \"dt\": 1712458800,\n",
      "            \"main\": {\n",
      "                \"temp\": 277.27,\n",
      "                \"feels_like\": 277.27,\n",
      "                \"temp_min\": 277.27,\n",
      "                \"temp_max\": 277.27,\n",
      "                \"pressure\": 1026,\n",
      "                \"sea_level\": 1026,\n",
      "                \"grnd_level\": 822,\n",
      "                \"humidity\": 89,\n",
      "                \"temp_kf\": 0\n",
      "            },\n",
      "            \"weather\": [\n",
      "                {\n",
      "                    \"id\": 802,\n",
      "                    \"main\": \"Clouds\",\n",
      "                    \"description\": \"scattered clouds\",\n",
      "                    \"icon\": \"03n\"\n",
      "                }\n",
      "            ],\n",
      "            \"clouds\": {\n",
      "                \"all\": 35\n",
      "            },\n",
      "            \"wind\": {\n",
      "                \"speed\": 0.92,\n",
      "                \"deg\": 344,\n",
      "                \"gust\": 0.84\n",
      "            },\n",
      "            \"visibility\": 10000,\n",
      "            \"pop\": 0,\n",
      "            \"sys\": {\n",
      "                \"pod\": \"n\"\n",
      "            },\n",
      "            \"dt_txt\": \"2024-04-07 03:00:00\"\n",
      "        },\n",
      "        {\n",
      "            \"dt\": 1712469600,\n",
      "            \"main\": {\n",
      "                \"temp\": 280.32,\n",
      "                \"feels_like\": 280.32,\n",
      "                \"temp_min\": 280.32,\n",
      "                \"temp_max\": 280.32,\n",
      "                \"pressure\": 1024,\n",
      "                \"sea_level\": 1024,\n",
      "                \"grnd_level\": 822,\n",
      "                \"humidity\": 82,\n",
      "                \"temp_kf\": 0\n",
      "            },\n",
      "            \"weather\": [\n",
      "                {\n",
      "                    \"id\": 802,\n",
      "                    \"main\": \"Clouds\",\n",
      "                    \"description\": \"scattered clouds\",\n",
      "                    \"icon\": \"03d\"\n",
      "                }\n",
      "            ],\n",
      "            \"clouds\": {\n",
      "                \"all\": 36\n",
      "            },\n",
      "            \"wind\": {\n",
      "                \"speed\": 0.99,\n",
      "                \"deg\": 308,\n",
      "                \"gust\": 0.71\n",
      "            },\n",
      "            \"visibility\": 10000,\n",
      "            \"pop\": 0,\n",
      "            \"sys\": {\n",
      "                \"pod\": \"d\"\n",
      "            },\n",
      "            \"dt_txt\": \"2024-04-07 06:00:00\"\n",
      "        },\n",
      "        {\n",
      "            \"dt\": 1712480400,\n",
      "            \"main\": {\n",
      "                \"temp\": 289.2,\n",
      "                \"feels_like\": 288.3,\n",
      "                \"temp_min\": 289.2,\n",
      "                \"temp_max\": 289.2,\n",
      "                \"pressure\": 1021,\n",
      "                \"sea_level\": 1021,\n",
      "                \"grnd_level\": 822,\n",
      "                \"humidity\": 55,\n",
      "                \"temp_kf\": 0\n",
      "            },\n",
      "            \"weather\": [\n",
      "                {\n",
      "                    \"id\": 801,\n",
      "                    \"main\": \"Clouds\",\n",
      "                    \"description\": \"few clouds\",\n",
      "                    \"icon\": \"02d\"\n",
      "                }\n",
      "            ],\n",
      "            \"clouds\": {\n",
      "                \"all\": 13\n",
      "            },\n",
      "            \"wind\": {\n",
      "                \"speed\": 1.04,\n",
      "                \"deg\": 281,\n",
      "                \"gust\": 1.01\n",
      "            },\n",
      "            \"visibility\": 10000,\n",
      "            \"pop\": 0,\n",
      "            \"sys\": {\n",
      "                \"pod\": \"d\"\n",
      "            },\n",
      "            \"dt_txt\": \"2024-04-07 09:00:00\"\n",
      "        },\n",
      "        {\n",
      "            \"dt\": 1712491200,\n",
      "            \"main\": {\n",
      "                \"temp\": 293.58,\n",
      "                \"feels_like\": 292.64,\n",
      "                \"temp_min\": 293.58,\n",
      "                \"temp_max\": 293.58,\n",
      "                \"pressure\": 1019,\n",
      "                \"sea_level\": 1019,\n",
      "                \"grnd_level\": 822,\n",
      "                \"humidity\": 37,\n",
      "                \"temp_kf\": 0\n",
      "            },\n",
      "            \"weather\": [\n",
      "                {\n",
      "                    \"id\": 802,\n",
      "                    \"main\": \"Clouds\",\n",
      "                    \"description\": \"scattered clouds\",\n",
      "                    \"icon\": \"03d\"\n",
      "                }\n",
      "            ],\n",
      "            \"clouds\": {\n",
      "                \"all\": 27\n",
      "            },\n",
      "            \"wind\": {\n",
      "                \"speed\": 0.28,\n",
      "                \"deg\": 243,\n",
      "                \"gust\": 0.85\n",
      "            },\n",
      "            \"visibility\": 10000,\n",
      "            \"pop\": 0,\n",
      "            \"sys\": {\n",
      "                \"pod\": \"d\"\n",
      "            },\n",
      "            \"dt_txt\": \"2024-04-07 12:00:00\"\n",
      "        },\n",
      "        {\n",
      "            \"dt\": 1712502000,\n",
      "            \"main\": {\n",
      "                \"temp\": 292.97,\n",
      "                \"feels_like\": 292.21,\n",
      "                \"temp_min\": 292.97,\n",
      "                \"temp_max\": 292.97,\n",
      "                \"pressure\": 1019,\n",
      "                \"sea_level\": 1019,\n",
      "                \"grnd_level\": 821,\n",
      "                \"humidity\": 46,\n",
      "                \"temp_kf\": 0\n",
      "            },\n",
      "            \"weather\": [\n",
      "                {\n",
      "                    \"id\": 804,\n",
      "                    \"main\": \"Clouds\",\n",
      "                    \"description\": \"overcast clouds\",\n",
      "                    \"icon\": \"04d\"\n",
      "                }\n",
      "            ],\n",
      "            \"clouds\": {\n",
      "                \"all\": 100\n",
      "            },\n",
      "            \"wind\": {\n",
      "                \"speed\": 0.77,\n",
      "                \"deg\": 230,\n",
      "                \"gust\": 0.75\n",
      "            },\n",
      "            \"visibility\": 10000,\n",
      "            \"pop\": 0,\n",
      "            \"sys\": {\n",
      "                \"pod\": \"d\"\n",
      "            },\n",
      "            \"dt_txt\": \"2024-04-07 15:00:00\"\n",
      "        }\n",
      "    ],\n",
      "    \"city\": {\n",
      "        \"id\": 3168508,\n",
      "        \"name\": \"Innichen\",\n",
      "        \"coord\": {\n",
      "            \"lat\": 46.7406,\n",
      "            \"lon\": 12.2797\n",
      "        },\n",
      "        \"country\": \"IT\",\n",
      "        \"population\": 3107,\n",
      "        \"timezone\": 7200,\n",
      "        \"sunrise\": 1712033256,\n",
      "        \"sunset\": 1712079646\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import json\n",
    "\n",
    "city = \"India\"  \n",
    "api_key = \"5741e64b3c0a404b61d6aaa71dcbdc98\" \n",
    "url = f\"https://api.openweathermap.org/data/2.5/forecast?q={city}&appid={api_key}\"\n",
    "response = requests.get(url)\n",
    "\n",
    "# Check if the request was successful\n",
    "if response.status_code == 200:\n",
    "    # Load JSON data from the response\n",
    "    data = response.json()\n",
    "    \n",
    "    # Pretty print the JSON data\n",
    "    pretty_json = json.dumps(data, indent=4)\n",
    "    print(pretty_json)\n",
    "else:\n",
    "    print(f\"Error: {response.status_code}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "class WeatherData:\n",
    "    def __init__(self, city):\n",
    "        self.city = city\n",
    "\n",
    "    def get_historical_data(self):\n",
    "        # Fetch historical weather data using an API (e.g., OpenWeatherMap)\n",
    "        url = f\"https://api.openweathermap.org/data/2.5/forecast?q={self.city}&appid=5741e64b3c0a404b61d6aaa71dcbdc98\"\n",
    "        response = requests.get(url)\n",
    "        if response.status_code == 200:  # Check if the API call was successful\n",
    "            data = response.json()\n",
    "            if 'list' in data:  # Check if the expected 'list' key is in the JSON response\n",
    "                # Extract relevant weather parameters\n",
    "                dates = [item['dt_txt'] for item in data['list']]\n",
    "                temperatures = [item['main']['temp'] for item in data['list']]\n",
    "                humidities = [item['main']['humidity'] for item in data['list']]\n",
    "                pressures = [item['main']['pressure'] for item in data['list']]\n",
    "                # Create a pandas DataFrame to store the data\n",
    "                df = pd.DataFrame({\n",
    "                    'Date': dates,\n",
    "                    'Temperature': temperatures,\n",
    "                    'Humidity': humidities,\n",
    "                    'Pressure': pressures\n",
    "                })\n",
    "                df['Date'] = pd.to_datetime(df['Date'])\n",
    "                return df\n",
    "            else:\n",
    "                print(\"The 'list' key is not in the response. Here's the full JSON response:\")\n",
    "                print(data)\n",
    "        else:\n",
    "            print(f\"API call failed with status code {response.status_code}\")\n",
    "        return None  # Ensure that None is returned if the API call fails or 'list' is not in the response\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "class ForecastModel:\n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "\n",
    "    def preprocess_data(self):\n",
    "        if self.data is not None:  # Check if the data is not None before proceeding\n",
    "            self.data.dropna(inplace=True)\n",
    "        else:\n",
    "            print(\"No data to preprocess.\")\n",
    "\n",
    "\n",
    "\n",
    "    def train_model(self):\n",
    "        # Split data into features and target variable\n",
    "        X = self.data[['Humidity', 'Pressure']]\n",
    "        y = self.data['Temperature']\n",
    "\n",
    "        # Split the data into training and testing sets\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "        # Train a linear regression model\n",
    "        model = LinearRegression()\n",
    "        model.fit(X_train, y_train)\n",
    "\n",
    "        return model\n",
    "\n",
    "    def plot_predictions(self, model):\n",
    "        # Plot predicted vs. actual temperature\n",
    "        predictions = model.predict(self.data[['Humidity', 'Pressure']])\n",
    "        plt.figure(figsize=(10, 6))\n",
    "        plt.scatter(self.data['Temperature'], predictions)\n",
    "        plt.plot([self.data['Temperature'].min(), self.data['Temperature'].max()], \n",
    "                 [self.data['Temperature'].min(), self.data['Temperature'].max()], 'k--', lw=2)\n",
    "        plt.xlabel('Actual Temperature')\n",
    "        plt.ylabel('Predicted Temperature')\n",
    "        plt.title('Actual vs. Predicted Temperature')\n",
    "        plt.grid(True)\n",
    "        plt.show()\n",
    "\n",
    "def main():\n",
    "    # Initialize WeatherData object for a specific city\n",
    "    city = 'New York'\n",
    "    weather_data = WeatherData(city)\n",
    "\n",
    "    # Fetch historical weather data\n",
    "    historical_data = weather_data.get_historical_data()\n",
    "\n",
    "    # Initialize ForecastModel object with the historical data\n",
    "    forecast_model = ForecastModel(historical_data)\n",
    "\n",
    "    # Preprocess the data\n",
    "    forecast_model.preprocess_data()\n",
    "\n",
    "    # Train the forecasting model\n",
    "    model = forecast_model.train_model()\n",
    "\n",
    "    # Plot the predictions\n",
    "    forecast_model.plot_predictions(model)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  }
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