diff --git a/Bitcoin price prediction/Bitcoin price prediction.ipynb b/Bitcoin price prediction/Bitcoin price prediction.ipynb new file mode 100644 index 0000000..c8cbc1a --- /dev/null +++ b/Bitcoin price prediction/Bitcoin price prediction.ipynb @@ -0,0 +1,1436 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bitcoin price prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#importing libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#load dataset\n", + "df= pd.read_csv('BitcoinPrice.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DatePrice
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12018-08-26 00:00:006673.274167
22018-08-27 00:00:006719.266154
32018-08-28 00:00:007000.040000
42018-08-29 00:00:007054.276429
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DatePrice
3602019-08-20 00:00:0010746.507692
3612019-08-21 00:00:0010169.094167
3622019-08-22 00:00:0010030.746667
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Price
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PricePrediction
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PricePrediction
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4548.7975 ,\n", + " 4309.3375 , 4293.84083333, 3823.51166667, 3920.53666667,\n", + " 3751.66833333, 4103.45384615, 4263.78333333, 4106.87166667,\n", + " 4116.7775 , 4167.54666667, 3967.52416667, 3961.49333333,\n", + " 3858.34916667, 3742.94333333, 3405.64333333, 3435.34 ,\n", + " 3528.80333333, 3523.96 , 3426.19 , 3462.04 ,\n", + " 3406.7625 , 3278.37416667, 3225.29916667, 3271.23833333,\n", + " 3392.405 , 3567.47 , 3808.0425 , 3982.85083333,\n", + " 4015.60916667, 3910.97333333, 4027.47833333, 4178.59083333,\n", + " 3813.88 , 3825.37916667, 3747.83916667, 3743.905 ,\n", + " 3912.28583333, 3832.92166667, 3791.54583333, 3752.27166667,\n", + " 3867.13833333, 3865.7975 , 3822.62666667, 3868.4875 ,\n", + " 3920.45666667, 4036.09333333, 4035.855 , 4034.13833333,\n", + " 3812.58583333, 3656.73583333, 3646.34583333, 3599.84166667,\n", + " 3604.1175 , 3656.785 , 3621.27083333, 3619.96416667,\n", + " 3632.395 , 3690.52333333, 3620.1275 , 3548.4275 ,\n", + " 3558.92416667, 3579.89666667, 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3933.95416667,\n", + " 4011.36583333, 4034.05666667, 4075.52642857, 4107.34083333,\n", + " 4109.31666667, 4145.10846154, 4675.1125 , 5018.49833333,\n", + " 4970.84916667, 4980.89833333, 5042.51769231, 5126.83416667,\n", + " 5214.27666667, 5197.75076923, 5251.19 , 5111.77076923,\n", + " 5031.475 , 5076.3 , 5077.805 , 5114.85416667,\n", + " 5109.94666667, 5214.57416667, 5263.3975 , 5255.61416667,\n", + " 5302.9575 , 5274.14583333, 5305.275 , 5527.80166667,\n", + " 5465.515 , 5421.52666667, 5280.61666667, 5281.80916667,\n", + " 5306.695 , 5277.88333333, 5262.36333333, 5310.17333333,\n", + " 5364.99166667, 5633.7475 , 5697.92333333, 5718.22916667,\n", + " 5654.35333333, 5863.52333333, 5851.67076923, 6056.4175 ,\n", + " 6302.6125 , 6833.35083333, 7152.48416667, 7447.11416667,\n", + " 8005.25083333, 8063.89583333, 7979.575 , 7265.96333333,\n", + " 7338.52083333, 7894.92666667, 7926.705 , 7954.80833333,\n", + " 7896.51083333, 7688.52583333, 7972.71916667, 8046.10769231,\n", + " 8114.9325 , 8779.97083333, 8727.90083333, 8646.195 ,\n", + " 8641.89166667, 8342.77 , 8543.03 , 8663.6425 ,\n", + " 8546.17166667, 7848.41583333, 7765.68833333, 7756.9575 ,\n", + " 7920.945 , 7941.22166667, 7817.76833333, 7815.13583333,\n", + " 7914.53916667, 8033.30666667, 8163.66333333, 8342.24076923,\n", + " 8721.645 , 9096.28583333, 9227.125 , 9160.0675 ,\n", + " 9147.005 , 9346.0725 , 9791.0175 , 10730.39166667,\n", + " 10748.01166667, 10851.84833333, 11314.76153846, 12686.38833333,\n", + " 11834.12416667, 11665.57583333, 11886.88615385, 11545.63333333,\n", + " 10690.83333333, 10300.4875 , 11342.3175 , 11779.45083333,\n", + " 11118.8875 , 11411.61666667, 11310.50666667, 11788.06916667,\n", + " 12567.70384615, 12668.62916667, 11560.6025 , 11577.69538462,\n", + " 11412.12416667, 10852.92666667, 10438.55416667, 10300.41166667,\n", + " 9584.47583333, 10092.75166667, 10455.73 , 10685.415 ,\n", + " 10569.305 , 10449.62666667, 10044.11333333, 9708.43583333,\n", + " 10021.325 , 9774.2575 , 9725.4025 , 9500.32416667,\n", + " 9533.97933333, 9539.7125 , 9873.81166667, 10088.8 ,\n", + " 10478.90166667, 10790.63 , 10826.275 , 11713.16166667,\n", + " 11759.01916667, 11703.73833333, 11803.88833333, 11816.9125 ,\n", + " 11586.1725 , 11377.80416667, 11397.80166667, 11144.38916667,\n", + " 10450.81333333, 9988.9475 , 10230.73333333, 10292.38333333,\n", + " 10295.1175 , 10605.82583333, 10746.50769231, 10169.09416667,\n", + " 10030.74666667, 10255.9775 , 10158.54083333])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# split the data\n", + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "P_days_array = np.array(df.drop(['Prediction'], 1))[-p_days: ]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 9774.2575 ]\n", + " [ 9725.4025 ]\n", + " [ 9500.32416667]\n", + " [ 9533.97933333]\n", + " [ 9539.7125 ]\n", + " [ 9873.81166667]\n", + " [10088.8 ]\n", + " [10478.90166667]\n", + " [10790.63 ]\n", + " [10826.275 ]\n", + " [11713.16166667]\n", + " [11759.01916667]\n", + " [11703.73833333]\n", + " [11803.88833333]\n", + " [11816.9125 ]\n", + " [11586.1725 ]\n", + " [11377.80416667]\n", + " [11397.80166667]\n", + " [11144.38916667]\n", + " [10450.81333333]\n", + " [ 9988.9475 ]\n", + " [10230.73333333]\n", + " [10292.38333333]\n", + " [10295.1175 ]\n", + " [10605.82583333]\n", + " [10746.50769231]\n", + " [10169.09416667]\n", + " [10030.74666667]\n", + " [10255.9775 ]\n", + " [10158.54083333]]\n" + ] + } + ], + "source": [ + "print(P_days_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random Forset Accuracy: 81.70%\n" + ] + } + ], + "source": [ + "Rf= RandomForestRegressor(n_estimators = 1000, random_state = 1)\n", + "Rf.fit(X_train, y_train)\n", + "print('Random Forset Accuracy: {:.2f}%'.format(Rf.score(X_test, y_test)*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10592.95281917 4139.74340925 4009.33721686 3750.94329917\n", + " 6686.03089333 8017.41007 4603.41209301 10917.573125\n", + " 10320.73992308 6382.47382974 7509.0243875 3744.34914776\n", + " 3687.6209075 5056.54525667 3747.92045345 4191.83126167\n", + " 10529.23178744 8470.04180417 6905.674225 5218.05125333\n", + " 4358.91695417 3638.28610218 5702.43225545 4455.19234582\n", + " 4202.18310936 10617.9656925 4066.20269833 7115.10845\n", + " 11412.84343769 4002.42426333 4206.6927183 3860.87866036\n", + " 7509.0243875 5303.99897417 11263.2550775 3748.50336583\n", + " 3997.12380519 5209.35112333 10126.78781538 3836.76394551\n", + " 4068.1524791 6825.921215 7128.49054583 10941.40453333\n", + " 7013.71267083 3688.69679871 10479.43125917 6332.03818083\n", + " 3831.54122147 10602.2699325 3831.54122147 10393.4880575\n", + " 4207.97078996 3761.95641686 3650.5778725 6727.83350083\n", + " 6508.72924583 4980.35495596 5941.3529375 10165.75482904\n", + " 3797.3707425 5724.37518583 3638.90940527 10590.86682936\n", + " 3672.98316681 4589.31359917 3816.34710583]\n" + ] + } + ], + "source": [ + "# Prediction\n", + "Rf_predict = Rf.predict(X_test)\n", + "print(Rf_predict)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10478.90166667 4109.31666667 8663.6425 3833.31083333\n", + " 6550.47416667 3808.0425 7688.52583333 9539.7125\n", + " 10044.11333333 6399.03333333 7447.11416667 5214.27666667\n", + " 3403.96416667 3832.92166667 3624.15583333 5306.695\n", + " 10851.84833333 7954.80833333 6568.54916667 5305.275\n", + " 6260.64583333 4025.02583333 4116.7775 4011.36583333\n", + " 3961.49333333 11560.6025 3933.95416667 6056.4175\n", + " 11665.57583333 4034.13833333 3417.1675 3630.39666667\n", + " 8005.25083333 5214.57416667 11397.80166667 4020.12583333\n", + " 4167.54666667 3558.92416667 10088.8 3827.4875\n", + " 4006.11583333 7765.68833333 5851.67076923 11586.1725\n", + " 8063.89583333 3589.26083333 9584.47583333 6411.28083333\n", + " 3609.86916667 10092.75166667 3645.27666667 10685.415\n", + " 4034.05666667 3225.29916667 3604.1175 5654.35333333\n", + " 6412.45916667 5111.77076923 5697.92333333 9160.0675\n", + " 3968.65666667 4548.7975 3567.29916667 11412.12416667\n", + " 5018.49833333 6299.39916667 5310.17333333]\n" + ] + } + ], + "source": [ + "print(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10592.95281917 4139.74340925 4009.33721686 3750.94329917\n", + " 6686.03089333 8017.41007 4603.41209301 10917.573125\n", + " 10320.73992308 6382.47382974 7509.0243875 3744.34914776\n", + " 3687.6209075 5056.54525667 3747.92045345 4191.83126167\n", + " 10529.23178744 8470.04180417 6905.674225 5218.05125333\n", + " 4358.91695417 3638.28610218 5702.43225545 4455.19234582\n", + " 4202.18310936 10617.9656925 4066.20269833 7115.10845\n", + " 11412.84343769 4002.42426333 4206.6927183 3860.87866036\n", + " 7509.0243875 5303.99897417 11263.2550775 3748.50336583\n", + " 3997.12380519 5209.35112333 10126.78781538 3836.76394551\n", + " 4068.1524791 6825.921215 7128.49054583 10941.40453333\n", + " 7013.71267083 3688.69679871 10479.43125917 6332.03818083\n", + " 3831.54122147 10602.2699325 3831.54122147 10393.4880575\n", + " 4207.97078996 3761.95641686 3650.5778725 6727.83350083\n", + " 6508.72924583 4980.35495596 5941.3529375 10165.75482904\n", + " 3797.3707425 5724.37518583 3638.90940527 10590.86682936\n", + " 3672.98316681 4589.31359917 3816.34710583]\n" + ] + } + ], + "source": [ + "# MOdel prediction for 30days\n", + "Rf_predict_30= Rf.predict(P_days_array)\n", + "print(Rf_predict)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PricePrediction
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values\n", + "df.tail(30)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Bitcoin price prediction/BitcoinPrice.csv b/Bitcoin price prediction/BitcoinPrice.csv new file mode 100644 index 0000000..0600ca4 --- /dev/null +++ b/Bitcoin price prediction/BitcoinPrice.csv @@ -0,0 +1,366 @@ +Date,Price +2018-08-25 00:00:00,6719.429230769231 +2018-08-26 00:00:00,6673.274166666666 +2018-08-27 00:00:00,6719.266153846153 +2018-08-28 00:00:00,7000.040000000002 +2018-08-29 00:00:00,7054.276428571429 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b/Bitcoin price prediction/Readme.txt @@ -0,0 +1,3 @@ +Bitcoin-price-Prediction-using-LSTM +Bitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network + diff --git a/FaceRecognition/README.md b/FaceRecognition/README.md index 612dde3..815ff33 100644 --- a/FaceRecognition/README.md +++ b/FaceRecognition/README.md @@ -2,7 +2,7 @@ Modern face recognition with deep learning and HOG algorithm. -1. Find faces in image (HOG Algorithm) +1. Find faces in image (HOG Algorithm) 2. Affine Transformations (Face alignment using an ensemble of regression trees) 3. Encoding Faces (FaceNet) @@ -42,4 +42,4 @@ Finally, we need a classifier (Linear SVM or other classifier) to find the perso Thanks to Adam Geitgey who wrote a great [post](https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78) about this, I followed his pipeline. -![Result](https://github.com/alexattia/Data-Science-Projects/blob/master/FaceRecognition/result.png) \ No newline at end of file +![Result](https://github.com/alexattia/Data-Science-Projects/blob/master/FaceRecognition/result.png) diff --git a/README.md b/README.md index cd6fd2f..1a77304 100644 --- a/README.md +++ b/README.md @@ -35,4 +35,6 @@ Deep Learning model (using Keras) to label satellite images. ## [Predicting IMDB movie rating](https://github.com/alexattia/Data-Science-Projects/tree/master/KaggleMovieRating) Project inspired by Chuan Sun [work](https://www.kaggle.com/deepmatrix/imdb-5000-movie-dataset) How can we tell the greatness of a movie ? -Scrapping and Machine Learning \ No newline at end of file +Scrapping and Machine Learning + +## [Bitcoin price prediction](https://github.com/connectaditya/Data-Science-Projects/tree/master/Bitcoin%20price%20prediction)