Upload gdp-forecaster (1).ipynb

gdp-forecaster (1).ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8a37d8d9",
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
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+     "iopub.status.idle": "2025-01-09T23:35:10.658005Z",
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+     "start_time": "2025-01-09T23:35:10.264792",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/kaggle/input/africa-gdp/Africa_GDP.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
+    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
+    "# For example, here's several helpful packages to load\n",
+    "\n",
+    "import numpy as np # linear algebra\n",
+    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
+    "\n",
+    "# Input data files are available in the read-only \"../input/\" directory\n",
+    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
+    "\n",
+    "import os\n",
+    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+    "    for filename in filenames:\n",
+    "        print(os.path.join(dirname, filename))\n",
+    "\n",
+    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
+    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3f865c92",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T23:35:10.666585Z",
+     "iopub.status.busy": "2025-01-09T23:35:10.666100Z",
+     "iopub.status.idle": "2025-01-09T23:35:10.683517Z",
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+    },
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+     "start_time": "2025-01-09T23:35:10.662847",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('/kaggle/input/africa-gdp/Africa_GDP.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6079ebb6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T23:35:10.691660Z",
+     "iopub.status.busy": "2025-01-09T23:35:10.691313Z",
+     "iopub.status.idle": "2025-01-09T23:35:10.710088Z",
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+    },
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+     "exception": false,
+     "start_time": "2025-01-09T23:35:10.688033",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data.fillna(data.mean(),inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2683890c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T23:35:10.718525Z",
+     "iopub.status.busy": "2025-01-09T23:35:10.718111Z",
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+    },
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+     "end_time": "2025-01-09T23:35:10.735403",
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+     "start_time": "2025-01-09T23:35:10.714776",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data.drop_duplicates(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5eff6792",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T23:35:10.741486Z",
+     "iopub.status.busy": "2025-01-09T23:35:10.741132Z",
+     "iopub.status.idle": "2025-01-09T23:35:12.076529Z",
+     "shell.execute_reply": "2025-01-09T23:35:12.075251Z"
+    },
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+     "end_time": "2025-01-09T23:35:12.078262",
+     "exception": false,
+     "start_time": "2025-01-09T23:35:10.737945",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Year       Algeria        Benin     Botswana  Burkina Faso      Burundi  \\\n",
+      "0  1960  2.723615e+09  226195578.4  30411413.66   330442815.8  195999990.0   \n",
+      "1  1961  2.434747e+09  235668220.5  32902612.87   350247234.3  202999992.0   \n",
+      "2  1962  2.001445e+09  236434954.0  35644956.64   379567099.2  213500006.0   \n",
+      "3  1963  2.702982e+09  253927697.3  38091842.85   394040667.1  232749998.0   \n",
+      "4  1964  2.909316e+09  269819005.9  41616347.79   410321645.0  260750008.0   \n",
+      "\n",
+      "      Cameroon  Central African Republic         Chad     Eswatini  ...  \\\n",
+      "0  614206068.5               112155598.5  313582728.1  35076845.97  ...   \n",
+      "1  652777608.3               123134583.5  333975336.1  43026042.79  ...   \n",
+      "2  694247864.4               124482773.8  357635713.4  45927961.63  ...   \n",
+      "3  718320845.0               129379123.8  371767002.2  54129438.35  ...   \n",
+      "4  776650176.9               142025078.7  392247517.7  64980554.01  ...   \n",
+      "\n",
+      "    Seychelles  Sierra Leone      Somalia  South Africa         Sudan  \\\n",
+      "0  12012024.62   322151470.6  180459936.8  8.748597e+09  1.127011e+09   \n",
+      "1  11592023.76   327979248.4  191659914.4  9.225996e+09  1.223563e+09   \n",
+      "2  12642025.92   342872712.4  203531927.5  9.813996e+09  1.329023e+09   \n",
+      "3  13923028.54   348700653.6  216145935.9  1.085420e+10  1.352011e+09   \n",
+      "4  15393031.56   372012091.5  229529912.7  1.195600e+10  1.389080e+09   \n",
+      "\n",
+      "       Tanzania         Togo       Uganda       Zambia      Zimbabwe  \n",
+      "0  2.651730e+09  171057069.1  423008385.7  713000000.0  1.052990e+09  \n",
+      "1  2.826179e+09  178497098.3  441524109.0  696285714.3  1.096647e+09  \n",
+      "2  3.101590e+09  186745757.9  449012578.6  693142857.1  1.117602e+09  \n",
+      "3  3.456579e+09  202305865.2  516147798.7  718714285.7  1.159512e+09  \n",
+      "4  3.748841e+09  234572186.5  589056603.8  839428571.4  1.217138e+09  \n",
+      "\n",
+      "[5 rows x 34 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "print(data.head())\n",
+    "X = data.drop('Algeria',axis=1)\n",
+    "y = data['Algeria']\n",
+    "\n",
+    "X_train, X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4d2ecbca",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T23:35:12.084723Z",
+     "iopub.status.busy": "2025-01-09T23:35:12.084288Z",
+     "iopub.status.idle": "2025-01-09T23:35:12.211636Z",
+     "shell.execute_reply": "2025-01-09T23:35:12.210433Z"
+    },
+    "papermill": {
+     "duration": 0.132424,
+     "end_time": "2025-01-09T23:35:12.213399",
+     "exception": false,
+     "start_time": "2025-01-09T23:35:12.080975",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error , mean_absolute_error, r2_score\n",
+    "\n",
+    "model = LinearRegression()\n",
+    "model.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "447cddae",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-09T23:35:12.220052Z",
+     "iopub.status.busy": "2025-01-09T23:35:12.219685Z",
+     "iopub.status.idle": "2025-01-09T23:35:12.230343Z",
+     "shell.execute_reply": "2025-01-09T23:35:12.229397Z"
+    },
+    "papermill": {
+     "duration": 0.015608,
+     "end_time": "2025-01-09T23:35:12.231921",
+     "exception": false,
+     "start_time": "2025-01-09T23:35:12.216313",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared error:9.904758040019183e+19\n",
+      "Mean Absolute Error:5989398763.378249\n",
+      "r2:0.9849119641737768\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\n",
+    "mse = mean_squared_error(y_test,y_pred)\n",
+    "mae = mean_absolute_error(y_test,y_pred)\n",
+    "r2 = r2_score(y_test,y_pred)\n",
+    "print(f\"Mean Squared error:{mse}\")\n",
+    "print(f\"Mean Absolute Error:{mae}\")\n",
+    "print(f\"r2:{r2}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "none",
+   "dataSources": [
+    {
+     "datasetId": 6455551,
+     "sourceId": 10415885,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "dockerImageVersionId": 30822,
+   "isGpuEnabled": false,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
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+   "display_name": "Python 3",
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+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
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+  },
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+   "end_time": "2025-01-09T23:35:12.854678",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2025-01-09T23:35:08.095462",
+   "version": "2.6.0"
+  }
+ },
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+ "nbformat_minor": 5
+}