Regression_Multiple Linear .ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ce52a52-a9a1-468a-9d9e-f65ea5e70b82",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4893a03f-cbc9-4b36-8dd8-d05face44c9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"https://raw.githubusercontent.com/KeithGalli/matplotlib_tutorial/master/fifa_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "58230e40-698b-495a-bc2f-e948b37f022d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Photo</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Flag</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>...</th>\n",
       "      <th>Composure</th>\n",
       "      <th>Marking</th>\n",
       "      <th>StandingTackle</th>\n",
       "      <th>SlidingTackle</th>\n",
       "      <th>GKDiving</th>\n",
       "      <th>GKHandling</th>\n",
       "      <th>GKKicking</th>\n",
       "      <th>GKPositioning</th>\n",
       "      <th>GKReflexes</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>193080</td>\n",
       "      <td>De Gea</td>\n",
       "      <td>27</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/193080.png</td>\n",
       "      <td>Spain</td>\n",
       "      <td>https://cdn.sofifa.org/flags/45.png</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>€138.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>200389</td>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>25</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/200389.png</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>https://cdn.sofifa.org/flags/44.png</td>\n",
       "      <td>90</td>\n",
       "      <td>93</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>€144.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>192448</td>\n",
       "      <td>M. ter Stegen</td>\n",
       "      <td>26</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/192448.png</td>\n",
       "      <td>Germany</td>\n",
       "      <td>https://cdn.sofifa.org/flags/21.png</td>\n",
       "      <td>89</td>\n",
       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>€123.3M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>192119</td>\n",
       "      <td>T. Courtois</td>\n",
       "      <td>26</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/192119.png</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>https://cdn.sofifa.org/flags/7.png</td>\n",
       "      <td>89</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>€113.7M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>167495</td>\n",
       "      <td>M. Neuer</td>\n",
       "      <td>32</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/167495.png</td>\n",
       "      <td>Germany</td>\n",
       "      <td>https://cdn.sofifa.org/flags/21.png</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>€62.7M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Unnamed: 0      ID           Name  Age  \\\n",
       "0      3           3  193080         De Gea   27   \n",
       "1      9           9  200389       J. Oblak   25   \n",
       "2     18          18  192448  M. ter Stegen   26   \n",
       "3     19          19  192119    T. Courtois   26   \n",
       "4     22          22  167495       M. Neuer   32   \n",
       "\n",
       "                                            Photo Nationality  \\\n",
       "0  https://cdn.sofifa.org/players/4/19/193080.png       Spain   \n",
       "1  https://cdn.sofifa.org/players/4/19/200389.png    Slovenia   \n",
       "2  https://cdn.sofifa.org/players/4/19/192448.png     Germany   \n",
       "3  https://cdn.sofifa.org/players/4/19/192119.png     Belgium   \n",
       "4  https://cdn.sofifa.org/players/4/19/167495.png     Germany   \n",
       "\n",
       "                                  Flag  Overall  Potential  ... Composure  \\\n",
       "0  https://cdn.sofifa.org/flags/45.png       91         93  ...      68.0   \n",
       "1  https://cdn.sofifa.org/flags/44.png       90         93  ...      70.0   \n",
       "2  https://cdn.sofifa.org/flags/21.png       89         92  ...      69.0   \n",
       "3   https://cdn.sofifa.org/flags/7.png       89         90  ...      66.0   \n",
       "4  https://cdn.sofifa.org/flags/21.png       89         89  ...      70.0   \n",
       "\n",
       "  Marking StandingTackle SlidingTackle  GKDiving GKHandling  GKKicking  \\\n",
       "0    15.0           21.0          13.0      90.0       85.0       87.0   \n",
       "1    27.0           12.0          18.0      86.0       92.0       78.0   \n",
       "2    25.0           13.0          10.0      87.0       85.0       88.0   \n",
       "3    20.0           18.0          16.0      85.0       91.0       72.0   \n",
       "4    17.0           10.0          11.0      90.0       86.0       91.0   \n",
       "\n",
       "   GKPositioning  GKReflexes Release Clause  \n",
       "0           88.0        94.0        €138.6M  \n",
       "1           88.0        89.0        €144.5M  \n",
       "2           85.0        90.0        €123.3M  \n",
       "3           86.0        88.0        €113.7M  \n",
       "4           87.0        87.0         €62.7M  \n",
       "\n",
       "[5 rows x 90 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df[df[\"Position\"]=='GK']\n",
    "df2.sort_values(by=\"Overall\")\n",
    "df2.reset_index(inplace=True)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "186e0884-30b5-4032-aaa8-af7d1c8b6d09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall</th>\n",
       "      <th>GKReflexes</th>\n",
       "      <th>GKDiving</th>\n",
       "      <th>GKPositioning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91</td>\n",
       "      <td>94.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90</td>\n",
       "      <td>89.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89</td>\n",
       "      <td>90.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89</td>\n",
       "      <td>88.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>89</td>\n",
       "      <td>87.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>48</td>\n",
       "      <td>51.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>48</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>48</td>\n",
       "      <td>46.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>47</td>\n",
       "      <td>44.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>47</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2025 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Overall  GKReflexes  GKDiving  GKPositioning\n",
       "0          91        94.0      90.0           88.0\n",
       "1          90        89.0      86.0           88.0\n",
       "2          89        90.0      87.0           85.0\n",
       "3          89        88.0      85.0           86.0\n",
       "4          89        87.0      90.0           87.0\n",
       "...       ...         ...       ...            ...\n",
       "2020       48        51.0      47.0           45.0\n",
       "2021       48        45.0      45.0           50.0\n",
       "2022       48        46.0      45.0           49.0\n",
       "2023       47        44.0      52.0           40.0\n",
       "2024       47        48.0      46.0           42.0\n",
       "\n",
       "[2025 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf=df2[['Overall','GKReflexes','GKDiving','GKPositioning']]\n",
    "cdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cf777774-5f0f-476c-ae8c-5943887088b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "msk=np.random.rand(len(df2))<0.8\n",
    "train=cdf[msk]\n",
    "test=cdf[~msk]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b782c5e1-00ed-47b8-8767-595afeb75d08",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 500x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, figsize=(5, 15))\n",
    "ax=axes [0]\n",
    "ax.scatter(cdf.GKReflexes,cdf.Overall, color='blue')\n",
    "ax.set_xlabel('GKReflexes')\n",
    "ax.set_ylabel('Overall')\n",
    "\n",
    "ax=axes[1]\n",
    "ax.scatter(cdf.GKDiving,cdf.Overall, color='blue')\n",
    "ax.set_xlabel('GKDiving')\n",
    "ax.set_ylabel('Overall')\n",
    "\n",
    "ax=axes[2]\n",
    "ax.scatter(cdf.GKPositioning,cdf.Overall, color='blue')\n",
    "ax.set_xlabel('GKPositioning')\n",
    "ax.set_ylabel('Overall')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4bc07fdd-63db-42af-8c54-7020328c6890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[0.27899674 0.30588989 0.37396893]]\n",
      "Intercept:  [2.60807332]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "regr=linear_model.LinearRegression()\n",
    "train_x=np.asanyarray(train[['GKReflexes','GKDiving','GKPositioning']])\n",
    "train_y=np.asanyarray(train[['Overall']])\n",
    "regr.fit(train_x,train_y)\n",
    "print('Coefficients: ', regr.coef_)\n",
    "print('Intercept: ', regr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "05ee2a0d-ae3d-4b4a-a573-d3774e2821f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 0.87\n",
      "Residual sum of squares (MSE): 1.33\n",
      "R2-score: 0.98\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "test_x=np.asanyarray(test[['GKReflexes','GKDiving','GKPositioning']])\n",
    "test_y=np.asanyarray(test[[\"Overall\"]])\n",
    "test_y_=regr.predict(test_x)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_-test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2F\" %np.mean((test_y_-test_y)**2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y,test_y_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c64f96f4-ff46-4544-94c7-fede49b10020",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}