pr2tik1

❗️ Closed issue #1 in veerkalburgi/veerkalburgi.github.io
❗️ Opened issue #1 in veerkalburgi/veerkalburgi.github.io
❗️ Opened issue #4 in orhanarifoglu/orhanarifoglu.github.io
🎉 Merged PR #2 in pr2tik1/activity-box

pr2tik1

import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow import keras 
import numpy as np
import os
import urllib.request

#Names of 10 Classes:
class_names = ['cloud', 'sun', 'pants', 'umbrella', 'table', 'ladder',
               'eyeglasses', 'clock', 'scissors', 'cup']

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import ast
from PIL import Image
import torchvision.transforms as transforms
from torch.autograd import Variable
import torchvision.models as models
from torch import __version__

resnet18 = models.resnet18(pretrained=True)
alexnet = models.alexnet(pretrained=True)
vgg16 = models.vgg16(pretrained=True)

pr2tik1

<img src="https://github.com/pr2tik1/pr2tik1/blob/master/IMAGE-NAME">

### Hi 👋
I am recent engineering graduate looking for opportunities and collabaration in projects related to data science and deep learning.
- 🔭 I’m currently working on image classification (also, I am brushing up my data structures and algorithms skills regularly).
- 🌱 I’m currently learning Computer Vision and Deep Learning techniques using PyTorch.
- 🤝 I’m looking to collaborate on data science and deep learning projects. 

![YOUR github stats](https://github-readme-stats.vercel.app/api?username=USERNAME)

pr2tik1

	ctryname	cowcode2	politycode	un_region_name	un_continent_name	ehead	leaderspellreg	democracy	regime	start_year	duration	observed
1	Afghanistan	700	700.0	Southern Asia	Asia	Mohammad Zahir Shah	Mohammad Zahir Shah.Afghanistan.1946.1952.Monarchy	Non-democracy	Monarchy	1946	7	1
2	Afghanistan	700	700.0	Southern Asia	Asia	Sardar Mohammad Daoud	Sardar Mohammad Daoud.Afghanistan.1953.1962.Civilian Dict	Non-democracy	Civilian Dict	1953	10	1
3	Afghanistan	700	700.0	Southern Asia	Asia	Mohammad Zahir Shah	Mohammad Zahir Shah.Afghanistan.1963.1972.Monarchy	Non-democracy	Monarchy	1963	10	1
4	Afghanistan	700	700.0	Southern Asia	Asia	Sardar Mohammad Daoud	Sardar Mohammad Daoud.Afghanistan.1973.1977.Civilian Dict	Non-democracy	Civilian Dict	1973	5	0
5	Afghanistan	700	700.0	Southern Asia	Asia	Nur Mohammad Taraki	Nur Mohammad Taraki.Afghanistan.1978.1978.Civilian Dict	Non-democracy	Civilian Dict	1978	1	0
6	Afghanistan	700	700.0	Southern Asia	Asia	Babrak Karmal	Babrak Karmal.Afghanistan.1979.1984.Civilian Dict	Non-democracy	Civ

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 def fixing_skewness(self):
        """
        Function takes in a dataframe and return fixed skewed dataframe
        """
        ## Getting all the data that are not of "object" type. 
        numeric = self.data.dtypes[self.data.dtypes != "object"].index

        # Check the skew of all numerical features
        skewed_feats = self.data[numeric].apply(lambda x: skew(x)).sort_values(ascending=False)
        high_skew = skewed_feats[abs(skewed_feats) > 0.5]

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def find_non_rare_labels(self, variable, tolerance):
        '''
        Function to check cardinality of a feature.
        Args: Dataframe, 
                Feature - Numerical Features,
                Tolerance - Threshold of number of values.
        Output: List of unique values of the feature.
        '''
        temp = self.data.groupby([variable])[variable].count() / len(self.data)
        non_rare = [x for x in temp.loc[temp>tolerance].index.values]

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def Imputation(self, var ,stats=False):
        '''
        Function to Impute data using sklearn Imputer
        Input : Dataframe, variable(continuous/categorical)
        Output : None
        '''
        if var==self.continuous:
            imputer= SimpleImputer(strategy='median')
        if var==self.categorical:
            imputer = SimpleImputer(strategy='most_frequent')

pr2tik1

def plot_missing(self,png=False):
        """
        Function to plot Missing data in the dataframe
        Input: dataframe
        Output: Bar-Plot
        """
        missing_data = self.data.isnull().sum()
        missing_df = pd.DataFrame(missing_data.drop(missing_data[missing_data == 0].index).sort_values(ascending=False),
                                 columns = ['values'])
        fig = px.bar(data_frame = missing_df, x = missing_df.index, y = missing_df.values, text =missing_df.values,