From 9403993141c06ff38250e2c1c3881bd74b870b4c Mon Sep 17 00:00:00 2001 From: Aditya kumar <36192662+connectaditya@users.noreply.github.com> Date: Sat, 25 Apr 2020 16:24:13 +0530 Subject: [PATCH 1/7] Changed --- FaceRecognition/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) 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) From e74dfa346a0de3fe7af526aeecb9912929423021 Mon Sep 17 00:00:00 2001 From: Aditya kumar <36192662+connectaditya@users.noreply.github.com> Date: Sat, 25 Apr 2020 16:54:11 +0530 Subject: [PATCH 2/7] Create Readme.txt --- Bitcoin price prediction/Readme.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 Bitcoin price prediction/Readme.txt diff --git a/Bitcoin price prediction/Readme.txt b/Bitcoin price prediction/Readme.txt new file mode 100644 index 0000000..110ed9b --- /dev/null +++ b/Bitcoin price prediction/Readme.txt @@ -0,0 +1 @@ +v From a9a07aa092bf124bb119121252866b4aa9bf2c33 Mon Sep 17 00:00:00 2001 From: Aditya kumar <36192662+connectaditya@users.noreply.github.com> Date: Sat, 25 Apr 2020 16:55:24 +0530 Subject: [PATCH 3/7] updated --- .../Bitcoin price prediction.ipynb | 1436 +++++++++++++++++ Bitcoin price prediction/BitcoinPrice.csv | 366 +++++ 2 files changed, 1802 insertions(+) create mode 100644 Bitcoin price prediction/Bitcoin price prediction.ipynb create mode 100644 Bitcoin price prediction/BitcoinPrice.csv 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": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DatePrice
02018-08-25 00:00:006719.429231
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
\n", + "
" + ], + "text/plain": [ + " Date Price\n", + "0 2018-08-25 00:00:00 6719.429231\n", + "1 2018-08-26 00:00:00 6673.274167\n", + "2 2018-08-27 00:00:00 6719.266154\n", + "3 2018-08-28 00:00:00 7000.040000\n", + "4 2018-08-29 00:00:00 7054.276429" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DatePrice
3602019-08-20 00:00:0010746.507692
3612019-08-21 00:00:0010169.094167
3622019-08-22 00:00:0010030.746667
3632019-08-23 00:00:0010255.977500
3642019-08-24 00:00:0010158.540833
\n", + "
" + ], + "text/plain": [ + " Date Price\n", + "360 2019-08-20 00:00:00 10746.507692\n", + "361 2019-08-21 00:00:00 10169.094167\n", + "362 2019-08-22 00:00:00 10030.746667\n", + "363 2019-08-23 00:00:00 10255.977500\n", + "364 2019-08-24 00:00:00 10158.540833" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 365 entries, 0 to 364\n", + "Data columns (total 2 columns):\n", + "Date 365 non-null object\n", + "Price 365 non-null float64\n", + "dtypes: float64(1), object(1)\n", + "memory usage: 5.8+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "df.drop(['Date'], 1, inplace= True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Price
06719.429231
16673.274167
26719.266154
37000.040000
47054.276429
\n", + "
" + ], + "text/plain": [ + " Price\n", + "0 6719.429231\n", + "1 6673.274167\n", + "2 6719.266154\n", + "3 7000.040000\n", + "4 7054.276429" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#predicting 30 days in future\n", + "p_days = 30\n", + "df['Prediction']=df[['Price']].shift(-p_days)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PricePrediction
06719.4292316639.304167
16673.2741676412.459167
26719.2661546468.631667
37000.0400006535.476667
47054.2764296677.342500
\n", + "
" + ], + "text/plain": [ + " Price Prediction\n", + "0 6719.429231 6639.304167\n", + "1 6673.274167 6412.459167\n", + "2 6719.266154 6468.631667\n", + "3 7000.040000 6535.476667\n", + "4 7054.276429 6677.342500" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PricePrediction
36010746.507692NaN
36110169.094167NaN
36210030.746667NaN
36310255.977500NaN
36410158.540833NaN
\n", + "
" + ], + "text/plain": [ + " Price Prediction\n", + "360 10746.507692 NaN\n", + "361 10169.094167 NaN\n", + "362 10030.746667 NaN\n", + "363 10255.977500 NaN\n", + "364 10158.540833 NaN" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# seperate X and Y\n", + "\n", + "X= np.array(df.drop(['Prediction'], 1))\n", + "X= X[: len(df) - p_days]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(335, 1)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 6719.42923077],\n", + " [ 6673.27416667],\n", + " [ 6719.26615385],\n", + " [ 7000.04 ],\n", + " [ 7054.27642857],\n", + " [ 6932.6625 ],\n", + " [ 6981.94615385],\n", + " [ 7100.94666667],\n", + " [ 7247.93538462],\n", + " [ 7260.94923077],\n", + " [ 7326.8525 ],\n", + " [ 7113.06923077],\n", + " [ 6433.27166667],\n", + " [ 6444.80416667],\n", + " [ 6366.1075 ],\n", + " [ 6286.42583333],\n", + " [ 6297.87769231],\n", + " [ 6296.32083333],\n", + " [ 6273.1375 ],\n", + " [ 6450.17923077],\n", + " [ 6499.0625 ],\n", + " [ 6518.655 ],\n", + " [ 6480.64416667],\n", + " [ 6400.60083333],\n", + " [ 6296.63166667],\n", + " [ 6335.82666667],\n", + " [ 6418.56266667],\n", + " [ 6669.99083333],\n", + " [ 6709.3125 ],\n", + " [ 6710.445 ],\n", + " [ 6639.30416667],\n", + " [ 6412.45916667],\n", + " [ 6468.63166667],\n", + " [ 6535.47666667],\n", + " [ 6677.3425 ],\n", + " [ 6550.47416667],\n", + " [ 6593.135 ],\n", + " [ 6590.96833333],\n", + " [ 6562.64166667],\n", + " [ 6470.4025 ],\n", + " [ 6563.62833333],\n", + " [ 6568.54916667],\n", + " [ 6581.48666667],\n", + " [ 6558.5375 ],\n", + " [ 6618.56769231],\n", + " [ 6621.71166667],\n", + " [ 6563.00916667],\n", + " [ 6248.63583333],\n", + " [ 6260.53083333],\n", + " [ 6260.64583333],\n", + " [ 6299.39916667],\n", + " [ 6452.57166667],\n", + " [ 6596.61833333],\n", + " [ 6596.27615385],\n", + " [ 6568.04076923],\n", + " [ 6487.44416667],\n", + " [ 6488.82583333],\n", + " [ 6531.60166667],\n", + " [ 6498.48583333],\n", + " [ 6481.426 ],\n", + " [ 6508.31 ],\n", + " [ 6478.0825 ],\n", + " [ 6473.75333333],\n", + " [ 6465.9175 ],\n", + " [ 6448.22166667],\n", + " [ 6382.66833333],\n", + " [ 6309.45285714],\n", + " [ 6310.28416667],\n", + " [ 6342.28083333],\n", + " [ 6387.67416667],\n", + " [ 6363.79583333],\n", + " [ 6391.87333333],\n", + " [ 6436.965 ],\n", + " [ 6445.35416667],\n", + " [ 6538.79 ],\n", + " [ 6486.25166667],\n", + " [ 6411.28083333],\n", + " [ 6399.03333333],\n", + " [ 6378.26833333],\n", + " [ 6401.93666667],\n", + " [ 6372.06333333],\n", + " [ 6176.155 ],\n", + " [ 5615.18 ],\n", + " [ 5596.1925 ],\n", + " [ 5558.24333333],\n", + " [ 5606.04416667],\n", + " [ 5303.9425 ],\n", + " [ 4671.97 ],\n", + " [ 4533.68083333],\n", + " [ 4548.7975 ],\n", + " [ 4309.3375 ],\n", + " [ 4293.84083333],\n", + " [ 3823.51166667],\n", + " [ 3920.53666667],\n", + " [ 3751.66833333],\n", + " [ 4103.45384615],\n", + " [ 4263.78333333],\n", + " [ 4106.87166667],\n", + " [ 4116.7775 ],\n", + " [ 4167.54666667],\n", + " [ 3967.52416667],\n", + " [ 3961.49333333],\n", + " [ 3858.34916667],\n", + " [ 3742.94333333],\n", + " [ 3405.64333333],\n", + " [ 3435.34 ],\n", + " [ 3528.80333333],\n", + " [ 3523.96 ],\n", + " [ 3426.19 ],\n", + " [ 3462.04 ],\n", + " [ 3406.7625 ],\n", + " [ 3278.37416667],\n", + " [ 3225.29916667],\n", + " [ 3271.23833333],\n", + " [ 3392.405 ],\n", + " [ 3567.47 ],\n", + " [ 3808.0425 ],\n", + " [ 3982.85083333],\n", + " [ 4015.60916667],\n", + " [ 3910.97333333],\n", + " [ 4027.47833333],\n", + " [ 4178.59083333],\n", + " [ 3813.88 ],\n", + " [ 3825.37916667],\n", + " [ 3747.83916667],\n", + " [ 3743.905 ],\n", + " [ 3912.28583333],\n", + " [ 3832.92166667],\n", + " [ 3791.54583333],\n", + " [ 3752.27166667],\n", + " [ 3867.13833333],\n", + " [ 3865.7975 ],\n", + " [ 3822.62666667],\n", + " [ 3868.4875 ],\n", + " [ 3920.45666667],\n", + " [ 4036.09333333],\n", + " [ 4035.855 ],\n", + " [ 4034.13833333],\n", + " [ 3812.58583333],\n", + " [ 3656.73583333],\n", + " [ 3646.34583333],\n", + " [ 3599.84166667],\n", + " [ 3604.1175 ],\n", + " [ 3656.785 ],\n", + " [ 3621.27083333],\n", + " [ 3619.96416667],\n", + " [ 3632.395 ],\n", + " [ 3690.52333333],\n", + " [ 3620.1275 ],\n", + " [ 3548.4275 ],\n", + " [ 3558.92416667],\n", + " [ 3579.89666667],\n", + " [ 3567.29916667],\n", + " [ 3572.735 ],\n", + " [ 3589.26083333],\n", + " [ 3567.76333333],\n", + " [ 3458.06666667],\n", + " [ 3417.1675 ],\n", + " [ 3452.22333333],\n", + " [ 3454.00882353],\n", + " [ 3454.19461538],\n", + " [ 3470.07916667],\n", + " [ 3468.305 ],\n", + " [ 3452.32833333],\n", + " [ 3458.70333333],\n", + " [ 3406.82083333],\n", + " [ 3403.96416667],\n", + " [ 3498.86833333],\n", + " [ 3652.21166667],\n", + " [ 3645.27666667],\n", + " [ 3634.77 ],\n", + " [ 3624.15583333],\n", + " [ 3623.07166667],\n", + " [ 3609.86916667],\n", + " [ 3610.74833333],\n", + " [ 3630.39666667],\n", + " [ 3631.55333333],\n", + " [ 3810.78153846],\n", + " [ 3933.14333333],\n", + " [ 3951.565 ],\n", + " [ 3948.41166667],\n", + " [ 3968.65666667],\n", + " [ 4018.04416667],\n", + " [ 4020.12583333],\n", + " [ 3829.56833333],\n", + " [ 3833.31083333],\n", + " [ 3832.61307692],\n", + " [ 3843.58333333],\n", + " [ 3842.13916667],\n", + " [ 3827.4875 ],\n", + " [ 3827.69083333],\n", + " [ 3749.56833333],\n", + " [ 3793.26083333],\n", + " [ 3863.33333333],\n", + " [ 3888.70166667],\n", + " [ 3905.05833333],\n", + " [ 3919.56583333],\n", + " [ 3922.615 ],\n", + " [ 3896.71833333],\n", + " [ 3880.7675 ],\n", + " [ 3881.37923077],\n", + " [ 3883.98928571],\n", + " [ 3918.08666667],\n", + " [ 4011.0925 ],\n", + " [ 3994.34833333],\n", + " [ 3998.4975 ],\n", + " [ 4008.65833333],\n", + " [ 4025.02583333],\n", + " [ 4026.63583333],\n", + " [ 4000.335 ],\n", + " [ 4006.11583333],\n", + " [ 3995.32333333],\n", + " [ 3977.45416667],\n", + " [ 3933.95416667],\n", + " [ 4011.36583333],\n", + " [ 4034.05666667],\n", + " [ 4075.52642857],\n", + " [ 4107.34083333],\n", + " [ 4109.31666667],\n", + " [ 4145.10846154],\n", + " [ 4675.1125 ],\n", + " [ 5018.49833333],\n", + " [ 4970.84916667],\n", + " [ 4980.89833333],\n", + " [ 5042.51769231],\n", + " [ 5126.83416667],\n", + " [ 5214.27666667],\n", + " [ 5197.75076923],\n", + " [ 5251.19 ],\n", + " [ 5111.77076923],\n", + " [ 5031.475 ],\n", + " [ 5076.3 ],\n", + " [ 5077.805 ],\n", + " [ 5114.85416667],\n", + " [ 5109.94666667],\n", + " [ 5214.57416667],\n", + " [ 5263.3975 ],\n", + " [ 5255.61416667],\n", + " [ 5302.9575 ],\n", + " [ 5274.14583333],\n", + " [ 5305.275 ],\n", + " [ 5527.80166667],\n", + " [ 5465.515 ],\n", + " [ 5421.52666667],\n", + " [ 5280.61666667],\n", + " [ 5281.80916667],\n", + " [ 5306.695 ],\n", + " [ 5277.88333333],\n", + " [ 5262.36333333],\n", + " [ 5310.17333333],\n", + " [ 5364.99166667],\n", + " [ 5633.7475 ],\n", + " [ 5697.92333333],\n", + " [ 5718.22916667],\n", + " [ 5654.35333333],\n", + " [ 5863.52333333],\n", + " [ 5851.67076923],\n", + " [ 6056.4175 ],\n", + " [ 6302.6125 ],\n", + " [ 6833.35083333],\n", + " [ 7152.48416667],\n", + " [ 7447.11416667],\n", + " [ 8005.25083333],\n", + " [ 8063.89583333],\n", + " [ 7979.575 ],\n", + " [ 7265.96333333],\n", + " [ 7338.52083333],\n", + " [ 7894.92666667],\n", + " [ 7926.705 ],\n", + " [ 7954.80833333],\n", + " [ 7896.51083333],\n", + " [ 7688.52583333],\n", + " [ 7972.71916667],\n", + " [ 8046.10769231],\n", + " [ 8114.9325 ],\n", + " [ 8779.97083333],\n", + " [ 8727.90083333],\n", + " [ 8646.195 ],\n", + " [ 8641.89166667],\n", + " [ 8342.77 ],\n", + " [ 8543.03 ],\n", + " [ 8663.6425 ],\n", + " [ 8546.17166667],\n", + " [ 7848.41583333],\n", + " [ 7765.68833333],\n", + " [ 7756.9575 ],\n", + " [ 7920.945 ],\n", + " [ 7941.22166667],\n", + " [ 7817.76833333],\n", + " [ 7815.13583333],\n", + " [ 7914.53916667],\n", + " [ 8033.30666667],\n", + " [ 8163.66333333],\n", + " [ 8342.24076923],\n", + " [ 8721.645 ],\n", + " [ 9096.28583333],\n", + " [ 9227.125 ],\n", + " [ 9160.0675 ],\n", + " [ 9147.005 ],\n", + " [ 9346.0725 ],\n", + " [ 9791.0175 ],\n", + " [10730.39166667],\n", + " [10748.01166667],\n", + " [10851.84833333],\n", + " [11314.76153846],\n", + " [12686.38833333],\n", + " [11834.12416667],\n", + " [11665.57583333],\n", + " [11886.88615385],\n", + " [11545.63333333],\n", + " [10690.83333333],\n", + " [10300.4875 ],\n", + " [11342.3175 ],\n", + " [11779.45083333],\n", + " [11118.8875 ],\n", + " [11411.61666667],\n", + " [11310.50666667],\n", + " [11788.06916667],\n", + " [12567.70384615],\n", + " [12668.62916667],\n", + " [11560.6025 ],\n", + " [11577.69538462],\n", + " [11412.12416667],\n", + " [10852.92666667],\n", + " [10438.55416667],\n", + " [10300.41166667],\n", + " [ 9584.47583333],\n", + " [10092.75166667],\n", + " [10455.73 ],\n", + " [10685.415 ],\n", + " [10569.305 ],\n", + " [10449.62666667],\n", + " [10044.11333333],\n", + " [ 9708.43583333],\n", + " [10021.325 ]])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# creating Y as np array\n", + "Y = np.array(df['Prediction'])\n", + "Y= Y[: -p_days]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(335,)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 6639.30416667, 6412.45916667, 6468.63166667, 6535.47666667,\n", + " 6677.3425 , 6550.47416667, 6593.135 , 6590.96833333,\n", + " 6562.64166667, 6470.4025 , 6563.62833333, 6568.54916667,\n", + " 6581.48666667, 6558.5375 , 6618.56769231, 6621.71166667,\n", + " 6563.00916667, 6248.63583333, 6260.53083333, 6260.64583333,\n", + " 6299.39916667, 6452.57166667, 6596.61833333, 6596.27615385,\n", + " 6568.04076923, 6487.44416667, 6488.82583333, 6531.60166667,\n", + " 6498.48583333, 6481.426 , 6508.31 , 6478.0825 ,\n", + " 6473.75333333, 6465.9175 , 6448.22166667, 6382.66833333,\n", + " 6309.45285714, 6310.28416667, 6342.28083333, 6387.67416667,\n", + " 6363.79583333, 6391.87333333, 6436.965 , 6445.35416667,\n", + " 6538.79 , 6486.25166667, 6411.28083333, 6399.03333333,\n", + " 6378.26833333, 6401.93666667, 6372.06333333, 6176.155 ,\n", + " 5615.18 , 5596.1925 , 5558.24333333, 5606.04416667,\n", + " 5303.9425 , 4671.97 , 4533.68083333, 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, 3567.29916667, 3572.735 ,\n", + " 3589.26083333, 3567.76333333, 3458.06666667, 3417.1675 ,\n", + " 3452.22333333, 3454.00882353, 3454.19461538, 3470.07916667,\n", + " 3468.305 , 3452.32833333, 3458.70333333, 3406.82083333,\n", + " 3403.96416667, 3498.86833333, 3652.21166667, 3645.27666667,\n", + " 3634.77 , 3624.15583333, 3623.07166667, 3609.86916667,\n", + " 3610.74833333, 3630.39666667, 3631.55333333, 3810.78153846,\n", + " 3933.14333333, 3951.565 , 3948.41166667, 3968.65666667,\n", + " 4018.04416667, 4020.12583333, 3829.56833333, 3833.31083333,\n", + " 3832.61307692, 3843.58333333, 3842.13916667, 3827.4875 ,\n", + " 3827.69083333, 3749.56833333, 3793.26083333, 3863.33333333,\n", + " 3888.70166667, 3905.05833333, 3919.56583333, 3922.615 ,\n", + " 3896.71833333, 3880.7675 , 3881.37923077, 3883.98928571,\n", + " 3918.08666667, 4011.0925 , 3994.34833333, 3998.4975 ,\n", + " 4008.65833333, 4025.02583333, 4026.63583333, 4000.335 ,\n", + " 4006.11583333, 3995.32333333, 3977.45416667, 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": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PricePrediction
3359774.257500NaN
3369725.402500NaN
3379500.324167NaN
3389533.979333NaN
3399539.712500NaN
3409873.811667NaN
34110088.800000NaN
34210478.901667NaN
34310790.630000NaN
34410826.275000NaN
34511713.161667NaN
34611759.019167NaN
34711703.738333NaN
34811803.888333NaN
34911816.912500NaN
35011586.172500NaN
35111377.804167NaN
35211397.801667NaN
35311144.389167NaN
35410450.813333NaN
3559988.947500NaN
35610230.733333NaN
35710292.383333NaN
35810295.117500NaN
35910605.825833NaN
36010746.507692NaN
36110169.094167NaN
36210030.746667NaN
36310255.977500NaN
36410158.540833NaN
\n", + "
" + ], + "text/plain": [ + " Price Prediction\n", + "335 9774.257500 NaN\n", + "336 9725.402500 NaN\n", + "337 9500.324167 NaN\n", + "338 9533.979333 NaN\n", + "339 9539.712500 NaN\n", + "340 9873.811667 NaN\n", + "341 10088.800000 NaN\n", + "342 10478.901667 NaN\n", + "343 10790.630000 NaN\n", + "344 10826.275000 NaN\n", + "345 11713.161667 NaN\n", + "346 11759.019167 NaN\n", + "347 11703.738333 NaN\n", + "348 11803.888333 NaN\n", + "349 11816.912500 NaN\n", + "350 11586.172500 NaN\n", + "351 11377.804167 NaN\n", + "352 11397.801667 NaN\n", + "353 11144.389167 NaN\n", + "354 10450.813333 NaN\n", + "355 9988.947500 NaN\n", + "356 10230.733333 NaN\n", + "357 10292.383333 NaN\n", + "358 10295.117500 NaN\n", + "359 10605.825833 NaN\n", + "360 10746.507692 NaN\n", + "361 10169.094167 NaN\n", + "362 10030.746667 NaN\n", + "363 10255.977500 NaN\n", + "364 10158.540833 NaN" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#orignal 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 +2018-08-30 00:00:00,6932.662499999999 +2018-08-31 00:00:00,6981.9461538461555 +2018-09-01 00:00:00,7100.946666666667 +2018-09-02 00:00:00,7247.9353846153845 +2018-09-03 00:00:00,7260.949230769231 +2018-09-04 00:00:00,7326.8525 +2018-09-05 00:00:00,7113.069230769231 +2018-09-06 00:00:00,6433.2716666666665 +2018-09-07 00:00:00,6444.804166666668 +2018-09-08 00:00:00,6366.107500000001 +2018-09-09 00:00:00,6286.425833333334 +2018-09-10 00:00:00,6297.877692307693 +2018-09-11 00:00:00,6296.320833333334 +2018-09-12 00:00:00,6273.1375 +2018-09-13 00:00:00,6450.179230769231 +2018-09-14 00:00:00,6499.0625 +2018-09-15 00:00:00,6518.655000000002 +2018-09-16 00:00:00,6480.644166666668 +2018-09-17 00:00:00,6400.600833333334 +2018-09-18 00:00:00,6296.631666666667 +2018-09-19 00:00:00,6335.826666666665 +2018-09-20 00:00:00,6418.562666666668 +2018-09-21 00:00:00,6669.990833333333 +2018-09-22 00:00:00,6709.312500000001 +2018-09-23 00:00:00,6710.445000000001 +2018-09-24 00:00:00,6639.304166666666 +2018-09-25 00:00:00,6412.459166666667 +2018-09-26 00:00:00,6468.631666666668 +2018-09-27 00:00:00,6535.4766666666665 +2018-09-28 00:00:00,6677.342500000002 +2018-09-29 00:00:00,6550.474166666667 +2018-09-30 00:00:00,6593.135000000001 +2018-10-01 00:00:00,6590.968333333334 +2018-10-02 00:00:00,6562.641666666666 +2018-10-03 00:00:00,6470.402500000001 +2018-10-04 00:00:00,6563.628333333333 +2018-10-05 00:00:00,6568.549166666667 +2018-10-06 00:00:00,6581.486666666667 +2018-10-07 00:00:00,6558.537499999999 +2018-10-08 00:00:00,6618.567692307693 +2018-10-09 00:00:00,6621.711666666667 +2018-10-10 00:00:00,6563.009166666667 +2018-10-11 00:00:00,6248.635833333334 +2018-10-12 00:00:00,6260.530833333333 +2018-10-13 00:00:00,6260.645833333335 +2018-10-14 00:00:00,6299.399166666666 +2018-10-15 00:00:00,6452.571666666667 +2018-10-16 00:00:00,6596.618333333333 +2018-10-17 00:00:00,6596.276153846155 +2018-10-18 00:00:00,6568.040769230769 +2018-10-19 00:00:00,6487.444166666667 +2018-10-20 00:00:00,6488.825833333333 +2018-10-21 00:00:00,6531.6016666666665 +2018-10-22 00:00:00,6498.485833333333 +2018-10-23 00:00:00,6481.425999999999 +2018-10-24 00:00:00,6508.31 +2018-10-25 00:00:00,6478.0825 +2018-10-26 00:00:00,6473.753333333334 +2018-10-27 00:00:00,6465.9175000000005 +2018-10-28 00:00:00,6448.221666666667 +2018-10-29 00:00:00,6382.668333333332 +2018-10-30 00:00:00,6309.4528571428555 +2018-10-31 00:00:00,6310.284166666667 +2018-11-01 00:00:00,6342.280833333334 +2018-11-02 00:00:00,6387.674166666667 +2018-11-03 00:00:00,6363.795833333334 +2018-11-04 00:00:00,6391.873333333333 +2018-11-05 00:00:00,6436.965 +2018-11-06 00:00:00,6445.354166666667 +2018-11-07 00:00:00,6538.79 +2018-11-08 00:00:00,6486.251666666667 +2018-11-09 00:00:00,6411.280833333333 +2018-11-10 00:00:00,6399.033333333333 +2018-11-11 00:00:00,6378.268333333333 +2018-11-12 00:00:00,6401.936666666667 +2018-11-13 00:00:00,6372.063333333333 +2018-11-14 00:00:00,6176.155 +2018-11-15 00:00:00,5615.179999999999 +2018-11-16 00:00:00,5596.1925 +2018-11-17 00:00:00,5558.243333333333 +2018-11-18 00:00:00,5606.044166666667 +2018-11-19 00:00:00,5303.9425 +2018-11-20 00:00:00,4671.97 +2018-11-21 00:00:00,4533.680833333334 +2018-11-22 00:00:00,4548.7975 +2018-11-23 00:00:00,4309.337500000001 +2018-11-24 00:00:00,4293.840833333333 +2018-11-25 00:00:00,3823.5116666666668 +2018-11-26 00:00:00,3920.5366666666664 +2018-11-27 00:00:00,3751.6683333333344 +2018-11-28 00:00:00,4103.453846153846 +2018-11-29 00:00:00,4263.783333333334 +2018-11-30 00:00:00,4106.871666666667 +2018-12-01 00:00:00,4116.777499999999 +2018-12-02 00:00:00,4167.546666666666 +2018-12-03 00:00:00,3967.5241666666666 +2018-12-04 00:00:00,3961.493333333334 +2018-12-05 00:00:00,3858.3491666666664 +2018-12-06 00:00:00,3742.943333333334 +2018-12-07 00:00:00,3405.6433333333334 +2018-12-08 00:00:00,3435.34 +2018-12-09 00:00:00,3528.8033333333333 +2018-12-10 00:00:00,3523.9600000000005 +2018-12-11 00:00:00,3426.19 +2018-12-12 00:00:00,3462.0399999999995 +2018-12-13 00:00:00,3406.7625000000007 +2018-12-14 00:00:00,3278.3741666666665 +2018-12-15 00:00:00,3225.299166666667 +2018-12-16 00:00:00,3271.238333333333 +2018-12-17 00:00:00,3392.405 +2018-12-18 00:00:00,3567.47 +2018-12-19 00:00:00,3808.0424999999996 +2018-12-20 00:00:00,3982.850833333334 +2018-12-21 00:00:00,4015.6091666666666 +2018-12-22 00:00:00,3910.973333333333 +2018-12-23 00:00:00,4027.478333333334 +2018-12-24 00:00:00,4178.590833333333 +2018-12-25 00:00:00,3813.879999999999 +2018-12-26 00:00:00,3825.3791666666675 +2018-12-27 00:00:00,3747.839166666666 +2018-12-28 00:00:00,3743.905 +2018-12-29 00:00:00,3912.285833333334 +2018-12-30 00:00:00,3832.9216666666666 +2018-12-31 00:00:00,3791.5458333333336 +2019-01-01 00:00:00,3752.271666666667 +2019-01-02 00:00:00,3867.138333333334 +2019-01-03 00:00:00,3865.7975 +2019-01-04 00:00:00,3822.626666666667 +2019-01-05 00:00:00,3868.4875000000006 +2019-01-06 00:00:00,3920.4566666666665 +2019-01-07 00:00:00,4036.093333333333 +2019-01-08 00:00:00,4035.8549999999996 +2019-01-09 00:00:00,4034.138333333333 +2019-01-10 00:00:00,3812.5858333333326 +2019-01-11 00:00:00,3656.7358333333327 +2019-01-12 00:00:00,3646.3458333333333 +2019-01-13 00:00:00,3599.841666666667 +2019-01-14 00:00:00,3604.1175 +2019-01-15 00:00:00,3656.7850000000003 +2019-01-16 00:00:00,3621.2708333333335 +2019-01-17 00:00:00,3619.9641666666666 +2019-01-18 00:00:00,3632.395 +2019-01-19 00:00:00,3690.523333333333 +2019-01-20 00:00:00,3620.1275 +2019-01-21 00:00:00,3548.4275 +2019-01-22 00:00:00,3558.924166666666 +2019-01-23 00:00:00,3579.896666666666 +2019-01-24 00:00:00,3567.299166666667 +2019-01-25 00:00:00,3572.735 +2019-01-26 00:00:00,3589.2608333333337 +2019-01-27 00:00:00,3567.763333333334 +2019-01-28 00:00:00,3458.066666666667 +2019-01-29 00:00:00,3417.1675 +2019-01-30 00:00:00,3452.223333333333 +2019-01-31 00:00:00,3454.008823529412 +2019-02-01 00:00:00,3454.194615384615 +2019-02-02 00:00:00,3470.0791666666664 +2019-02-03 00:00:00,3468.3050000000003 +2019-02-04 00:00:00,3452.3283333333334 +2019-02-05 00:00:00,3458.703333333333 +2019-02-06 00:00:00,3406.8208333333337 +2019-02-07 00:00:00,3403.9641666666666 +2019-02-08 00:00:00,3498.868333333334 +2019-02-09 00:00:00,3652.2116666666666 +2019-02-10 00:00:00,3645.2766666666666 +2019-02-11 00:00:00,3634.769999999999 +2019-02-12 00:00:00,3624.155833333334 +2019-02-13 00:00:00,3623.0716666666667 +2019-02-14 00:00:00,3609.869166666667 +2019-02-15 00:00:00,3610.7483333333334 +2019-02-16 00:00:00,3630.396666666666 +2019-02-17 00:00:00,3631.5533333333333 +2019-02-18 00:00:00,3810.7815384615387 +2019-02-19 00:00:00,3933.1433333333334 +2019-02-20 00:00:00,3951.565 +2019-02-21 00:00:00,3948.411666666667 +2019-02-22 00:00:00,3968.6566666666663 +2019-02-23 00:00:00,4018.0441666666666 +2019-02-24 00:00:00,4020.1258333333335 +2019-02-25 00:00:00,3829.568333333334 +2019-02-26 00:00:00,3833.3108333333334 +2019-02-27 00:00:00,3832.613076923077 +2019-02-28 00:00:00,3843.5833333333335 +2019-03-01 00:00:00,3842.1391666666664 +2019-03-02 00:00:00,3827.4874999999993 +2019-03-03 00:00:00,3827.6908333333326 +2019-03-04 00:00:00,3749.568333333333 +2019-03-05 00:00:00,3793.2608333333337 +2019-03-06 00:00:00,3863.3333333333335 +2019-03-07 00:00:00,3888.7016666666673 +2019-03-08 00:00:00,3905.058333333333 +2019-03-09 00:00:00,3919.5658333333336 +2019-03-10 00:00:00,3922.6150000000002 +2019-03-11 00:00:00,3896.718333333333 +2019-03-12 00:00:00,3880.7675 +2019-03-13 00:00:00,3881.3792307692306 +2019-03-14 00:00:00,3883.9892857142854 +2019-03-15 00:00:00,3918.0866666666666 +2019-03-16 00:00:00,4011.092500000001 +2019-03-17 00:00:00,3994.3483333333334 +2019-03-18 00:00:00,3998.4975 +2019-03-19 00:00:00,4008.6583333333333 +2019-03-20 00:00:00,4025.0258333333327 +2019-03-21 00:00:00,4026.6358333333333 +2019-03-22 00:00:00,4000.3350000000005 +2019-03-23 00:00:00,4006.1158333333333 +2019-03-24 00:00:00,3995.3233333333333 +2019-03-25 00:00:00,3977.4541666666664 +2019-03-26 00:00:00,3933.9541666666664 +2019-03-27 00:00:00,4011.3658333333333 +2019-03-28 00:00:00,4034.056666666666 +2019-03-29 00:00:00,4075.5264285714293 +2019-03-30 00:00:00,4107.340833333334 +2019-03-31 00:00:00,4109.316666666667 +2019-04-01 00:00:00,4145.108461538462 +2019-04-02 00:00:00,4675.1125 +2019-04-03 00:00:00,5018.498333333334 +2019-04-04 00:00:00,4970.849166666668 +2019-04-05 00:00:00,4980.8983333333335 +2019-04-06 00:00:00,5042.517692307692 +2019-04-07 00:00:00,5126.8341666666665 +2019-04-08 00:00:00,5214.276666666666 +2019-04-09 00:00:00,5197.750769230769 +2019-04-10 00:00:00,5251.1900000000005 +2019-04-11 00:00:00,5111.7707692307695 +2019-04-12 00:00:00,5031.474999999999 +2019-04-13 00:00:00,5076.3 +2019-04-14 00:00:00,5077.805 +2019-04-15 00:00:00,5114.854166666667 +2019-04-16 00:00:00,5109.946666666667 +2019-04-17 00:00:00,5214.574166666666 +2019-04-18 00:00:00,5263.397499999999 +2019-04-19 00:00:00,5255.614166666667 +2019-04-20 00:00:00,5302.9575 +2019-04-21 00:00:00,5274.145833333333 +2019-04-22 00:00:00,5305.275000000001 +2019-04-23 00:00:00,5527.801666666666 +2019-04-24 00:00:00,5465.514999999999 +2019-04-25 00:00:00,5421.526666666667 +2019-04-26 00:00:00,5280.616666666666 +2019-04-27 00:00:00,5281.809166666665 +2019-04-28 00:00:00,5306.695000000001 +2019-04-29 00:00:00,5277.883333333334 +2019-04-30 00:00:00,5262.363333333334 +2019-05-01 00:00:00,5310.173333333334 +2019-05-02 00:00:00,5364.991666666667 +2019-05-03 00:00:00,5633.7475 +2019-05-04 00:00:00,5697.923333333333 +2019-05-05 00:00:00,5718.229166666667 +2019-05-06 00:00:00,5654.353333333333 +2019-05-07 00:00:00,5863.5233333333335 +2019-05-08 00:00:00,5851.670769230769 +2019-05-09 00:00:00,6056.4175 +2019-05-10 00:00:00,6302.6125 +2019-05-11 00:00:00,6833.350833333334 +2019-05-12 00:00:00,7152.484166666666 +2019-05-13 00:00:00,7447.114166666666 +2019-05-14 00:00:00,8005.250833333334 +2019-05-15 00:00:00,8063.895833333333 +2019-05-16 00:00:00,7979.575000000001 +2019-05-17 00:00:00,7265.963333333332 +2019-05-18 00:00:00,7338.520833333332 +2019-05-19 00:00:00,7894.926666666666 +2019-05-20 00:00:00,7926.704999999999 +2019-05-21 00:00:00,7954.808333333333 +2019-05-22 00:00:00,7896.510833333333 +2019-05-23 00:00:00,7688.525833333332 +2019-05-24 00:00:00,7972.719166666667 +2019-05-25 00:00:00,8046.107692307693 +2019-05-26 00:00:00,8114.932499999999 +2019-05-27 00:00:00,8779.970833333333 +2019-05-28 00:00:00,8727.900833333333 +2019-05-29 00:00:00,8646.195000000002 +2019-05-30 00:00:00,8641.891666666668 +2019-05-31 00:00:00,8342.770000000002 +2019-06-01 00:00:00,8543.03 +2019-06-02 00:00:00,8663.6425 +2019-06-03 00:00:00,8546.171666666667 +2019-06-04 00:00:00,7848.4158333333335 +2019-06-05 00:00:00,7765.688333333334 +2019-06-06 00:00:00,7756.9575 +2019-06-07 00:00:00,7920.945000000001 +2019-06-08 00:00:00,7941.221666666667 +2019-06-09 00:00:00,7817.768333333333 +2019-06-10 00:00:00,7815.135833333333 +2019-06-11 00:00:00,7914.5391666666665 +2019-06-12 00:00:00,8033.306666666667 +2019-06-13 00:00:00,8163.663333333334 +2019-06-14 00:00:00,8342.24076923077 +2019-06-15 00:00:00,8721.645 +2019-06-16 00:00:00,9096.285833333333 +2019-06-17 00:00:00,9227.124999999998 +2019-06-18 00:00:00,9160.0675 +2019-06-19 00:00:00,9147.005 +2019-06-20 00:00:00,9346.0725 +2019-06-21 00:00:00,9791.017499999998 +2019-06-22 00:00:00,10730.391666666666 +2019-06-23 00:00:00,10748.011666666667 +2019-06-24 00:00:00,10851.848333333333 +2019-06-25 00:00:00,11314.76153846154 +2019-06-26 00:00:00,12686.38833333333 +2019-06-27 00:00:00,11834.124166666668 +2019-06-28 00:00:00,11665.575833333334 +2019-06-29 00:00:00,11886.886153846155 +2019-06-30 00:00:00,11545.633333333331 +2019-07-01 00:00:00,10690.833333333336 +2019-07-02 00:00:00,10300.487500000001 +2019-07-03 00:00:00,11342.3175 +2019-07-04 00:00:00,11779.450833333334 +2019-07-05 00:00:00,11118.887499999999 +2019-07-06 00:00:00,11411.616666666669 +2019-07-07 00:00:00,11310.506666666668 +2019-07-08 00:00:00,11788.069166666668 +2019-07-09 00:00:00,12567.703846153845 +2019-07-10 00:00:00,12668.629166666668 +2019-07-11 00:00:00,11560.6025 +2019-07-12 00:00:00,11577.695384615385 +2019-07-13 00:00:00,11412.124166666668 +2019-07-14 00:00:00,10852.926666666668 +2019-07-15 00:00:00,10438.554166666667 +2019-07-16 00:00:00,10300.411666666667 +2019-07-17 00:00:00,9584.475833333332 +2019-07-18 00:00:00,10092.751666666667 +2019-07-19 00:00:00,10455.73 +2019-07-20 00:00:00,10685.415 +2019-07-21 00:00:00,10569.304999999998 +2019-07-22 00:00:00,10449.626666666667 +2019-07-23 00:00:00,10044.113333333336 +2019-07-24 00:00:00,9708.435833333331 +2019-07-25 00:00:00,10021.325 +2019-07-26 00:00:00,9774.2575 +2019-07-27 00:00:00,9725.4025 +2019-07-28 00:00:00,9500.324166666665 +2019-07-29 00:00:00,9533.979333333333 +2019-07-30 00:00:00,9539.7125 +2019-07-31 00:00:00,9873.811666666668 +2019-08-01 00:00:00,10088.799999999997 +2019-08-02 00:00:00,10478.901666666667 +2019-08-03 00:00:00,10790.63 +2019-08-04 00:00:00,10826.275000000001 +2019-08-05 00:00:00,11713.161666666667 +2019-08-06 00:00:00,11759.019166666667 +2019-08-07 00:00:00,11703.738333333335 +2019-08-08 00:00:00,11803.88833333333 +2019-08-09 00:00:00,11816.912499999999 +2019-08-10 00:00:00,11586.1725 +2019-08-11 00:00:00,11377.804166666669 +2019-08-12 00:00:00,11397.801666666666 +2019-08-13 00:00:00,11144.38916666667 +2019-08-14 00:00:00,10450.813333333334 +2019-08-15 00:00:00,9988.947499999998 +2019-08-16 00:00:00,10230.733333333332 +2019-08-17 00:00:00,10292.383333333333 +2019-08-18 00:00:00,10295.117500000002 +2019-08-19 00:00:00,10605.825833333334 +2019-08-20 00:00:00,10746.507692307692 +2019-08-21 00:00:00,10169.094166666666 +2019-08-22 00:00:00,10030.746666666666 +2019-08-23 00:00:00,10255.9775 +2019-08-24 00:00:00,10158.540833333334 From bf033d21eec351a25202552708cb7371b7265aa2 Mon Sep 17 00:00:00 2001 From: Aditya kumar <36192662+connectaditya@users.noreply.github.com> Date: Sat, 25 Apr 2020 16:57:59 +0530 Subject: [PATCH 4/7] change --- Bitcoin price prediction/Readme.txt | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/Bitcoin price prediction/Readme.txt b/Bitcoin price prediction/Readme.txt index 110ed9b..8cffeba 100644 --- a/Bitcoin price prediction/Readme.txt +++ b/Bitcoin price prediction/Readme.txt @@ -1 +1,3 @@ -v +Bitcoin-price-Prediction-using-LSTM +Bitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network + From 76eb374d4e119656a152fdfa0a1c7cc0c23d12d0 Mon Sep 17 00:00:00 2001 From: Aditya kumar <36192662+connectaditya@users.noreply.github.com> Date: Sat, 25 Apr 2020 16:58:28 +0530 Subject: [PATCH 5/7] change --- Bitcoin price prediction/Readme.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Bitcoin price prediction/Readme.txt b/Bitcoin price prediction/Readme.txt index 8cffeba..c09f8b8 100644 --- a/Bitcoin price prediction/Readme.txt +++ b/Bitcoin price prediction/Readme.txt @@ -1,3 +1,3 @@ -Bitcoin-price-Prediction-using-LSTM +# Bitcoin-price-Prediction-using-LSTM Bitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network From 1c94da12c91d19b4b8830aaccd521dbb1c377e54 Mon Sep 17 00:00:00 2001 From: Aditya kumar <36192662+connectaditya@users.noreply.github.com> Date: Sat, 25 Apr 2020 16:58:51 +0530 Subject: [PATCH 6/7] Changed --- Bitcoin price prediction/Readme.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Bitcoin price prediction/Readme.txt b/Bitcoin price prediction/Readme.txt index c09f8b8..8cffeba 100644 --- a/Bitcoin price prediction/Readme.txt +++ b/Bitcoin price prediction/Readme.txt @@ -1,3 +1,3 @@ -# Bitcoin-price-Prediction-using-LSTM +Bitcoin-price-Prediction-using-LSTM Bitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network From ecc46e96f1a0c44f81f90e33221d0c87cf929948 Mon Sep 17 00:00:00 2001 From: Aditya kumar <36192662+connectaditya@users.noreply.github.com> Date: Sat, 25 Apr 2020 17:01:06 +0530 Subject: [PATCH 7/7] changed --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) 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)