DL2 Tesla Stock Price Prediction .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Data Pre processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Tesla.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         0\n",
       "High         0\n",
       "Low          0\n",
       "Close        0\n",
       "Volume       0\n",
       "Adj Close    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "Collapsed": "false"
   },
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6/29/2010</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.00</td>\n",
       "      <td>17.540001</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>18766300</td>\n",
       "      <td>23.889999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6/30/2010</td>\n",
       "      <td>25.790001</td>\n",
       "      <td>30.42</td>\n",
       "      <td>23.299999</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>17187100</td>\n",
       "      <td>23.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7/1/2010</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.92</td>\n",
       "      <td>20.270000</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>8218800</td>\n",
       "      <td>21.959999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7/2/2010</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.10</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>5139800</td>\n",
       "      <td>19.200001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>7/6/2010</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.00</td>\n",
       "      <td>15.830000</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>6866900</td>\n",
       "      <td>16.110001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date       Open   High        Low      Close    Volume  Adj Close\n",
       "0  6/29/2010  19.000000  25.00  17.540001  23.889999  18766300  23.889999\n",
       "1  6/30/2010  25.790001  30.42  23.299999  23.830000  17187100  23.830000\n",
       "2   7/1/2010  25.000000  25.92  20.270000  21.959999   8218800  21.959999\n",
       "3   7/2/2010  23.000000  23.10  18.709999  19.200001   5139800  19.200001\n",
       "4   7/6/2010  20.000000  20.00  15.830000  16.110001   6866900  16.110001"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df.drop(\"Date\",1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "data_x = df.drop(columns=\"Adj Close\" ,).values.tolist()\n",
    "data_y = df[\"Adj Close\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([19.0, 25.0, 17.540001, 23.889999, 18766300.0], 23.889999)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x[0] , data_y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1692"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Building Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn import functional as F\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "device = \"cpu\"\n",
    "if torch.cuda.is_available():\n",
    "    device = \"cuda\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "train_x , train_y , test_x , test_y = np.array(data_x[:1000]) , np.array(data_y[:1000]) , np.array(data_x[1000:]) , np.array(data_y[1000:])\n",
    "\n",
    "train_x = torch.from_numpy(train_x).float().to(device)\n",
    "train_y = torch.from_numpy(train_y).float().to(device)\n",
    "\n",
    "test_x = torch.from_numpy(test_x).float().to(device)\n",
    "test_y = torch.from_numpy(test_y).float().to(device)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.input = nn.Linear(5 , 40)\n",
    "        self.h1 = nn.Linear(40  , 100)\n",
    "        self.h2 = nn.Linear(100, 60)\n",
    "        self.out = nn.Linear(60 , 1)\n",
    "        self.activation = nn.ReLU()\n",
    "    \n",
    "    def forward(self , x):\n",
    "        x = self.activation(self.input(x))\n",
    "        x = self.activation(self.h1(x))\n",
    "        x = self.activation(self.h2(x))\n",
    "        x = self.out(x)\n",
    "        return x\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net(\n",
       "  (input): Linear(in_features=5, out_features=40, bias=True)\n",
       "  (h1): Linear(in_features=40, out_features=100, bias=True)\n",
       "  (h2): Linear(in_features=100, out_features=60, bias=True)\n",
       "  (out): Linear(in_features=60, out_features=1, bias=True)\n",
       "  (activation): ReLU()\n",
       ")"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Net()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "model = Net().to(device)\n",
    "\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters() , lr=0.0001)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "from  tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Training Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1000 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/torch/nn/modules/loss.py:431: UserWarning: Using a target size (torch.Size([1000])) that is different to the input size (torch.Size([1000, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n",
      "100%|██████████| 1000/1000 [00:02<00:00, 463.96it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Loss Value')"
      ]
     },
     "execution_count": 16,
     "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": [
    "epochs = 1000\n",
    "evey = 10\n",
    "loss_vals = []\n",
    "plt_x = []\n",
    "\n",
    "for epoch in tqdm(range(epochs)):\n",
    "    \n",
    "    y = model(train_x)\n",
    "    loss = criterion(y , train_y)    \n",
    "    \n",
    "    if epoch % evey ==0 :\n",
    "#         print( f\"Epoch:{epoch}  , Loss:{loss.squeeze_()}\")\n",
    "        \n",
    "        loss_vals.append(loss.data.item())\n",
    "        plt_x.append(epoch)\n",
    "    \n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    model.zero_grad()\n",
    "    \n",
    "plt.plot(plt_x , loss_vals , label=\"Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss Value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct:0 , Total:692 ,Accuracy 0.0 %\n"
     ]
    }
   ],
   "source": [
    "correct = total = 0\n",
    "test_x = test_x.to(device)\n",
    "test_y = test_y.to(device)\n",
    "\n",
    "for i in range(len(test_x)):\n",
    "    output = model.forward(test_x[i])\n",
    "#     print(int(output.data.item() ), int(test_y[i]))\n",
    "    if int(output.data.item() ) == int(test_y[i]):\n",
    "        correct +=1\n",
    "    total +=1\n",
    "print(f\"Correct:{correct} , Total:{total} ,Accuracy {correct/total * 100} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "train_x , train_y , test_x , test_y = np.array(data_x[:1000]) , np.array(data_y[:1000]) , np.array(data_x[1000:]) , np.array(data_y[1000:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "model = LinearRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_x , train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :100.0 %\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy :{model.score(test_x , test_y) * 100} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Sometimes it's good to use Machine Learning instead of Deep Learning , when we have less amount of data."
   ]
  },
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