CalCOFI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8920b23-9f52-44ff-a8a1-79615303fc5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ff12178-185b-4c2b-855f-474a5852981a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/Amit/Desktop/CalCOFI.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7be76b3d-fd3d-4c26-acae-3925af5b0963",
   "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>Cst_Cnt</th>\n",
       "      <th>Btl_Cnt</th>\n",
       "      <th>Depthm</th>\n",
       "      <th>T_degC</th>\n",
       "      <th>Salnty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>33.440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>10.46</td>\n",
       "      <td>33.440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>10.46</td>\n",
       "      <td>33.437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>10.45</td>\n",
       "      <td>33.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>10.45</td>\n",
       "      <td>33.421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cst_Cnt  Btl_Cnt  Depthm  T_degC  Salnty\n",
       "0        1        1       0   10.50  33.440\n",
       "1        1        2       8   10.46  33.440\n",
       "2        1        3      10   10.46  33.437\n",
       "3        1        4      19   10.45  33.420\n",
       "4        1        5      20   10.45  33.421"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "612a6620-3390-49c0-92a4-833573d630d4",
   "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>Cst_Cnt</th>\n",
       "      <th>Btl_Cnt</th>\n",
       "      <th>Depthm</th>\n",
       "      <th>T_degC</th>\n",
       "      <th>Salnty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.500</td>\n",
       "      <td>33.4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>10.460</td>\n",
       "      <td>33.4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>10.460</td>\n",
       "      <td>33.4370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>10.450</td>\n",
       "      <td>33.4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>10.450</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>864858</th>\n",
       "      <td>34404</td>\n",
       "      <td>864859</td>\n",
       "      <td>0</td>\n",
       "      <td>18.744</td>\n",
       "      <td>33.4083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864859</th>\n",
       "      <td>34404</td>\n",
       "      <td>864860</td>\n",
       "      <td>2</td>\n",
       "      <td>18.744</td>\n",
       "      <td>33.4083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864860</th>\n",
       "      <td>34404</td>\n",
       "      <td>864861</td>\n",
       "      <td>5</td>\n",
       "      <td>18.692</td>\n",
       "      <td>33.4150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864861</th>\n",
       "      <td>34404</td>\n",
       "      <td>864862</td>\n",
       "      <td>10</td>\n",
       "      <td>18.161</td>\n",
       "      <td>33.4062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864862</th>\n",
       "      <td>34404</td>\n",
       "      <td>864863</td>\n",
       "      <td>15</td>\n",
       "      <td>17.533</td>\n",
       "      <td>33.3880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>864863 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cst_Cnt  Btl_Cnt  Depthm  T_degC   Salnty\n",
       "0             1        1       0  10.500  33.4400\n",
       "1             1        2       8  10.460  33.4400\n",
       "2             1        3      10  10.460  33.4370\n",
       "3             1        4      19  10.450  33.4200\n",
       "4             1        5      20  10.450  33.4210\n",
       "...         ...      ...     ...     ...      ...\n",
       "864858    34404   864859       0  18.744  33.4083\n",
       "864859    34404   864860       2  18.744  33.4083\n",
       "864860    34404   864861       5  18.692  33.4150\n",
       "864861    34404   864862      10  18.161  33.4062\n",
       "864862    34404   864863      15  17.533  33.3880\n",
       "\n",
       "[864863 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a4f90dae-1d92-47e7-8e74-b3012745845e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm = sns.heatmap(df.corr(), cmap = \"YlGnBu\", annot= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b42d86a9-6617-4bb4-9ee2-94ae5968853d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df[['Salnty','T_degC']] # Water Temperature is dependent variable, Salinity is independent variable\n",
    "df2 = df2[:][:1000] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "63221bc3-7f34-4310-9175-b033c983fd32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Salnty</th>\n",
       "      <th>T_degC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.440</td>\n",
       "      <td>10.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.440</td>\n",
       "      <td>10.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.437</td>\n",
       "      <td>10.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.420</td>\n",
       "      <td>10.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.421</td>\n",
       "      <td>10.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Salnty  T_degC\n",
       "0  33.440   10.50\n",
       "1  33.440   10.46\n",
       "2  33.437   10.46\n",
       "3  33.420   10.45\n",
       "4  33.421   10.45"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4950049-6560-44a3-8858-1686e59291e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Salnty</th>\n",
       "      <th>T_degC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.440</td>\n",
       "      <td>10.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.440</td>\n",
       "      <td>10.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.437</td>\n",
       "      <td>10.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.420</td>\n",
       "      <td>10.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.421</td>\n",
       "      <td>10.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>34.040</td>\n",
       "      <td>7.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>34.040</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>34.049</td>\n",
       "      <td>6.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>34.113</td>\n",
       "      <td>5.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>34.160</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>989 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Salnty  T_degC\n",
       "0    33.440   10.50\n",
       "1    33.440   10.46\n",
       "2    33.437   10.46\n",
       "3    33.420   10.45\n",
       "4    33.421   10.45\n",
       "..      ...     ...\n",
       "995  34.040    7.22\n",
       "996  34.040    6.58\n",
       "997  34.049    6.44\n",
       "998  34.113    5.85\n",
       "999  34.160    5.60\n",
       "\n",
       "[989 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop_duplicates(subset=['Salnty','T_degC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "117f88a9-ee09-4fad-9b01-36f9e1306f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy=\"most_frequent\")\n",
    "imputer.fit(df2)\n",
    "df2 = imputer.transform(df2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "496e0ab2-2acb-43b6-afab-1ea75880ba8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.43570713,  0.62784363],\n",
       "       [-0.41802309,  0.62784363],\n",
       "       [-0.44946138,  0.56298631],\n",
       "       ...,\n",
       "       [ 0.77859665, -0.61062247],\n",
       "       [ 0.90434979, -0.79284067],\n",
       "       [ 0.99669976, -0.87005178]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "Scaler = StandardScaler()\n",
    "df2 = Scaler.fit_transform(df2)\n",
    "df2[5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "de9e03f7-18d4-4a77-a5e5-388572180adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df2[:,0:1].reshape(-1,1)\n",
    "y = df2[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9ab88342-9e80-4483-b663-788f99c12830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Temp')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.title(\"Temp vs Salinity\")\n",
    "plt.xlabel(\"Salinity\")\n",
    "plt.ylabel(\"Temp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a133168f-1f58-41b9-8040-e8d6e086886b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "80502f46-9c19-424f-b255-1252664548da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "lr = linear_model.LinearRegression()\n",
    "lr.fit(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f549e62b-8b82-4ad1-8e74-b921264d7121",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_data = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "86d0aa5e-59a2-424e-bc45-0827dcbd5909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Temp')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test,y_test,color=\"red\")\n",
    "plt.scatter(x_test, pred_data, color=\"blue\")\n",
    "plt.title(\"Temp vs Salinity (Linear Regression)\")\n",
    "plt.xlabel(\"Salinity\")\n",
    "plt.ylabel(\"Temp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c5ce7ee4-8bba-48b6-a55c-b3f1933de592",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6032832083927516\n"
     ]
    }
   ],
   "source": [
    "r_square = lr.score(x_test,y_test)\n",
    "print(r_square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "969b977e-18b5-4339-81b0-439d6e724547",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}