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K. Ali Pardhan alik604

Full time student
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Python & Machine Learning Hello world

Quickly get up to speed

Software setup

Use Python 3.8.*. No point in 3.9, however it should be fine.

Run these to test if install is successful, and install some important packages. Please follow the errors as they come, one will ask the user to install C++ build tools, if not already installed (see below). This will take several minutes, as the dependencies are a few GBs. You must be off VPN, or set-proxy, as shown below.

alik604 / starred repo
Last active Mar 11, 2021
Open all starred GitHub repositories in new tab - Open every starred GitHub repo in chrome, with python
View starred repo
import requests
import json
import webbrowser
USER = "alik604"
chrome_path = 'C:/Program Files (x86)/Google/Chrome/Application/chrome.exe %s'
# Change to your OS -
req = requests.get('' + USER + '/starred?per_page=100')
# ?page=2&per_page=100 # page is not working, first result is always the same
alik604 /
Last active Aug 24, 2020 — forked from tartakynov/
Fourier Extrapolation in Python
import numpy as np
import pylab as pl
from numpy import fft
def fourierExtrapolation(x, n_predict):
n = x.size
n_harm = 10 # number of harmonics in model
t = np.arange(0, n)
p = np.polyfit(t, x, 1) # find linear trend in x
View mnist - get
!pip install mnist
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import metrics
import mnist
# from hmmlearn.hmm import GaussianHMM, MultinomialHMM
X_train = mnist.train_images()
import urllib.parse as parse
url = ""
if 'url' not in globals():
print("Enter URL to parse")
url = input()
def printDict(data):
for x in data:
alik604 / build_dataset
Created Jul 28, 2020
View build_dataset
# make dataset to input
def build_dataset(time_series, seq_length):
dataX = []
dataY = []
for i in range(0, len(time_series) - seq_length):
_x = time_series[i:i + seq_length, :]
_y = time_series[i + seq_length, [-1]] # Next close price
print(_x, "->", _y)
alik604 / visualizing data in 2d and
Last active Jul 4, 2020
Quickly visualize your data in 2d and 3d with PCA and TSNE (t-sne)
View visualizing data in 2d and
# imports from matplotlib import pyplot as plt
from matplotlib import pyplot as plt
import pylab
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import proj3d
%matplotlib inline
%pylab inline
from sklearn.manifold import TSNE
from sklear.decomposition import PCA
alik604 /
Last active Jul 1, 2020
boiler plate for jupyter notebook on Data Science or Machine Learning
!pip install scikit-plot
!pip install catboost
!pip install mlxtend
!pip install yfinance
!pip install pyod
import pyod
import yfinance
import xgboost #
View numba_absolute_minimum.ipynb
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alik604 /
Created Jun 4, 2020
Image augmentation by mirroring, random rotation, shifts, shear and flips, etc.
# @credit -
import os
# Input data files are available in the "../input/" directory.
# Any results you write to the current directory are saved as output.
import numpy as np
#import pandas as pd # pd.read_csv
import cv2
import random