├── README.md ├── Stock Price Prediction.docx ├── Stock Price Prediction.pdf ├── Stock Price Prediction_synopsis.pdf ├── Stocks_code.ipynb ├── Stocks_code.ipynb - Colaboratory.pdf └── stocks_code.py /README.md: -------------------------------------------------------------------------------- 1 | # Stock-Price-Prediction 2 | 3 | Top Class Stock Price Prediction Project through Machine Learning Algorithms for Google. Easy Understanding and Implementation. 4 | 5 | Stock Price Prediction 6 | Stock (also known as equity) is a security that represents the ownership of a fraction of a corporation. This entitles the owner of the stock to a proportion of the corporation's assets and profits equal to how much stock they own. Units of stock are called "shares." 7 | A stock is a general term used to describe the ownership certificates of any company. 8 | Stock prices change everyday by market forces. By this we mean that share prices change because of supply and demand. If more people want to buy a stock (demand) than sell it (supply), then the price moves up. Conversely, if more people wanted to sell a stock than buy it, there would be greater supply than demand, and the price would fall. 9 | Understanding supply and demand is easy. 10 | So, why do stock prices change? The best answer is that nobody really knows for sure. Some believe that it isn't possible to predict how stocks will change in price while others think that by drawing charts and looking at past price movements, you can determine when to buy and sell. The only thing we do know as a certainty is that stocks are volatile and can change in price extremely rapidly. 11 | 12 | 13 | ## Understanding the Problem Statement 14 | We’ll dive into the implementation part of this Project soon, but first it’s important to establish what we’re aiming to solve. Broadly, stock market analysis is divided into two parts – Fundamental Analysis and Technical Analysis. 15 | Fundamental Analysis involves analyzing the company’s future profitability on the basis of its current business environment and financial performance. 16 | Technical Analysis, on the other hand, includes reading the charts and using statistical figures to identify the trends in the stock market. 17 | As you might have guessed, our focus will be on the technical analysis and visualization part. We’ll be using a dataset from Google stock Price test and train. 18 | 19 | ### Youtube Video of this Project: https://www.youtube.com/watch?v=44u5oU9MQGg 20 | 21 | ## Implementation: 22 | Using Sckiit Learning( Machine Learning model) 23 | Data Preprocessing using dataset 24 | Visualization of Dataset 25 | Feature Scaling 26 | Preparing the Datasets for training 27 | Reshaping the datasets 28 | Model development 29 | Implementation of sequential, dense, LSTM and dropout. 30 | Preprocessing the Data 31 | Predicting the Output 32 | Result visualization 33 | 34 | Research Paper: 35 | Project is totally based on research papers as project predict output using LSTM based deep learning models 36 | https://arxiv.org/abs/2009.10819 37 | 38 | https://www.aclweb.org/anthology/W19-6403.pdf 39 | 40 | https://www.sciencedirect.com/science/article/pii/S1877050920304865 41 | 42 | 43 | 44 | 45 |

नमस्ते (Namaste) 🙏🏻 !

46 |

We are Project Developers ❤

47 | 48 |

You Can use this Beautiful Project for your college Project and get good marks too ❤

49 | 50 | Email me Now **vatshayan007@gmail.com** to get this Full Project Code, PPT, Report, Synopsis, Video Presentation and Research paper of this Project. 51 | 52 | 💌 Feel free to contact me for any kind of help on projects. 53 | 54 | ### 📫 HOW TO REACH ME 55 | 56 | 💬 WhatsApp: **[LINK](https://wa.me/message/CHWN2AHCPMAZK1) : +91 9310631437 (Helping 24*7)** 57 | 58 | 💬 Gmail: **vatshayan007@gmail.com** 59 | 60 | Other Final Year Project: https://github.com/Vatshayan/Final-Year-Project-Cryptographic-Technique-for-Communication-System 61 | 62 | ### Youtube Video of this Project: https://www.youtube.com/watch?v=44u5oU9MQGg 63 | 64 | ### CSE Projects: [LINK](https://www.cse-projects.com) 65 | -------------------------------------------------------------------------------- /Stock Price Prediction.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Projects-Developer/Google-Stock-Price-Prediction-by-Deep-Learning/9417328f16d53668da2262739bf0e0b6e5491413/Stock Price Prediction.docx -------------------------------------------------------------------------------- /Stock Price Prediction.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Projects-Developer/Google-Stock-Price-Prediction-by-Deep-Learning/9417328f16d53668da2262739bf0e0b6e5491413/Stock Price Prediction.pdf -------------------------------------------------------------------------------- /Stock Price Prediction_synopsis.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Projects-Developer/Google-Stock-Price-Prediction-by-Deep-Learning/9417328f16d53668da2262739bf0e0b6e5491413/Stock Price Prediction_synopsis.pdf -------------------------------------------------------------------------------- /Stocks_code.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Stocks_code.ipynb", 7 | "provenance": [] 8 | }, 9 | "kernelspec": { 10 | "name": "python3", 11 | "display_name": "Python 3" 12 | }, 13 | "language_info": { 14 | "name": "python" 15 | } 16 | }, 17 | "cells": [ 18 | { 19 | "cell_type": "code", 20 | "metadata": { 21 | "id": "453JDspxl3pA" 22 | }, 23 | "source": [ 24 | "import numpy as np\n", 25 | "import matplotlib.pyplot as plt\n", 26 | "import pandas as pd\n" 27 | ], 28 | "execution_count": 1, 29 | "outputs": [] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": { 34 | "id": "9QSy7UtlnREU" 35 | }, 36 | "source": [ 37 | "**Data Preprocessing**\n", 38 | "\n", 39 | "\n" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "metadata": { 45 | "colab": { 46 | "base_uri": "https://localhost:8080/" 47 | }, 48 | "id": "zGunPw9amYyB", 49 | "outputId": "9c027b71-30de-404a-c5a7-d9af8955d92c" 50 | }, 51 | "source": [ 52 | "#loading the Data\n", 53 | "dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')\n", 54 | "print('shape is = {}'.format(dataset_train.shape))\n", 55 | "print(dataset_train.head())" 56 | ], 57 | "execution_count": 2, 58 | "outputs": [ 59 | { 60 | "output_type": "stream", 61 | "text": [ 62 | "shape is = (1258, 6)\n", 63 | " Date Open High Low Close Volume\n", 64 | "0 1/3/2012 325.25 332.83 324.97 663.59 7,380,500\n", 65 | "1 1/4/2012 331.27 333.87 329.08 666.45 5,749,400\n", 66 | "2 1/5/2012 329.83 330.75 326.89 657.21 6,590,300\n", 67 | "3 1/6/2012 328.34 328.77 323.68 648.24 5,405,900\n", 68 | "4 1/9/2012 322.04 322.29 309.46 620.76 11,688,800\n" 69 | ], 70 | "name": "stdout" 71 | } 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "metadata": { 77 | "colab": { 78 | "base_uri": "https://localhost:8080/" 79 | }, 80 | "id": "0-a2op74mh0F", 81 | "outputId": "ed394edf-457c-41b8-f092-62bbb4dc0d4a" 82 | }, 83 | "source": [ 84 | "training_set = dataset_train.iloc[:,1:2].values\n", 85 | "print('shape is ={}'.format(training_set.shape))\n", 86 | "print(training_set[0:5])" 87 | ], 88 | "execution_count": 3, 89 | "outputs": [ 90 | { 91 | "output_type": "stream", 92 | "text": [ 93 | "shape is =(1258, 1)\n", 94 | "[[325.25]\n", 95 | " [331.27]\n", 96 | " [329.83]\n", 97 | " [328.34]\n", 98 | " [322.04]]\n" 99 | ], 100 | "name": "stdout" 101 | } 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "metadata": { 107 | "colab": { 108 | "base_uri": "https://localhost:8080/", 109 | "height": 279 110 | }, 111 | "id": "8ZTCeWQtmk5i", 112 | "outputId": "7103d987-c399-4887-eefa-5b6ab6e684c4" 113 | }, 114 | "source": [ 115 | "#Visualizing the Data\n", 116 | "plt.plot(training_set, color = 'red', label = 'Google Stock Price in Test set')\n", 117 | "plt.xlabel('Time')\n", 118 | "plt.ylabel('Google Stock Price')\n", 119 | "plt.legend()\n", 120 | "plt.show()\n" 121 | ], 122 | "execution_count": 4, 123 | "outputs": [ 124 | { 125 | "output_type": "display_data", 126 | "data": { 127 | "image/png": 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\n", 128 | "text/plain": [ 129 | "
" 130 | ] 131 | }, 132 | "metadata": { 133 | "tags": [], 134 | "needs_background": "light" 135 | } 136 | } 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "metadata": { 142 | "id": "od5mUHZDmpK6" 143 | }, 144 | "source": [ 145 | "" 146 | ], 147 | "execution_count": null, 148 | "outputs": [] 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "metadata": { 153 | "id": "zpiznVbOmqpD" 154 | }, 155 | "source": [ 156 | "Hi WAIT WAIT !\n", 157 | "Project code is more longer with detailed description.\n", 158 | "\n", 159 | "If you want Project Code, synopsis and Report then Please mail me at vatshayan007@gmail.com " 160 | ] 161 | }, 162 | { 163 | "cell_type": "markdown", 164 | "metadata": { 165 | "id": "MbMBYPzZm9yv" 166 | }, 167 | "source": [ 168 | "## Mail me at vatshayan007@gmail.com for the Project files now." 169 | ] 170 | }, 171 | { 172 | "cell_type": "code", 173 | "metadata": { 174 | "id": "412G7yB8m8te" 175 | }, 176 | "source": [ 177 | "" 178 | ], 179 | "execution_count": null, 180 | "outputs": [] 181 | }, 182 | { 183 | "cell_type": "code", 184 | "metadata": { 185 | "id": "3QhRc5blmpE8" 186 | }, 187 | "source": [ 188 | "" 189 | ], 190 | "execution_count": null, 191 | "outputs": [] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "metadata": { 196 | "id": "8mXF5Dwzmo7C" 197 | }, 198 | "source": [ 199 | "" 200 | ], 201 | "execution_count": null, 202 | "outputs": [] 203 | }, 204 | { 205 | "cell_type": "markdown", 206 | "metadata": { 207 | "id": "zjO3yx9BmojC" 208 | }, 209 | "source": [ 210 | "" 211 | ] 212 | } 213 | ] 214 | } -------------------------------------------------------------------------------- /Stocks_code.ipynb - Colaboratory.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Projects-Developer/Google-Stock-Price-Prediction-by-Deep-Learning/9417328f16d53668da2262739bf0e0b6e5491413/Stocks_code.ipynb - Colaboratory.pdf -------------------------------------------------------------------------------- /stocks_code.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """Stocks_code.ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/1x896lhi9KwX9JZwLTP5mXGbhjtWHKIjT 8 | """ 9 | 10 | import numpy as np 11 | import matplotlib.pyplot as plt 12 | import pandas as pd 13 | 14 | """**Data Preprocessing** 15 | 16 | 17 | 18 | """ 19 | 20 | #loading the Data 21 | dataset_train = pd.read_csv('Google_Stock_Price_Train.csv') 22 | print('shape is = {}'.format(dataset_train.shape)) 23 | print(dataset_train.head()) 24 | 25 | training_set = dataset_train.iloc[:,1:2].values 26 | print('shape is ={}'.format(training_set.shape)) 27 | print(training_set[0:5]) 28 | 29 | #Visualizing the Data 30 | plt.plot(training_set, color = 'red', label = 'Google Stock Price in Test set') 31 | plt.xlabel('Time') 32 | plt.ylabel('Google Stock Price') 33 | plt.legend() 34 | plt.show() 35 | 36 | 37 | 38 | """Hi WAIT WAIT ! 39 | Project code is more longer with detailed description. 40 | 41 | If you want Project Code, synopsis and Report then Please mail me at vatshayan007@gmail.com 42 | 43 | ## Mail me at vatshayan007@gmail.com for the Project files now. 44 | """ 45 | 46 | 47 | 48 | 49 | 50 | --------------------------------------------------------------------------------