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❗️ 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 |
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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) |
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<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) |
We can make this file beautiful and searchable if this error is corrected: It looks like row 7 should actually have 13 columns, instead of 10. in line 6.
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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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time | Died | group | |||
---|---|---|---|---|---|
1 | 485 | 0 | 1 | ||
2 | 526 | 1 | 2 | ||
3 | 588 | 1 | 2 | ||
4 | 997 | 0 | 1 | ||
5 | 426 | 1 | 1 | ||
6 | 625 | 0 | 1 | ||
7 | 564 | 0 | 1 | ||
8 | 543 | 0 | 1 | ||
9 | 523 | 0 | 1 |
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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') |
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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, |
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