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128023684/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df_third.groupby(['cust_id', 'prod_cat_code', 'prod_subcat_code'])['transaction_id'].count().to_dict()
df_combined = pd.merge(df_third, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
grp_df = df_combined.groupby(['cust_id', 'prod_cat', 'prod_subcat', 'tran_date'])['tran_date', 'prod_subcat'].count()
df_third.columns
df_third_part = df_third[df_third.Rate > 0].reset_index()
df_combined = pd.merge(df_third_part, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
md = pd.DataFrame(df_combined[['cust_id', 'prod_cat', 'prod_subcat', 'tran_date']].value_counts())
combined_pair = df_combined[['prod_cat', 'Store_type']].drop_duplicates().to_dict('records')
contribution_dictionary = {}
for j in range(len(combined_pair)):
df_sub_part = df_combined[(df_combined.prod_cat == combined_pair[j]['prod_cat']) & (df_combined.Store_type == combined_pair[j]['Store_type'])].reset_index()
contribution_dictionary[combined_pair[j]['prod_cat'], combined_pair[j]['Store_type']] = np.round(100 * df_sub_part['total_amt'].sum() / df_combined['total_amt'].sum(), 2)
import operator
sorted_x = {k: v for k, v in sorted(contribution_dictionary.items(), key=lambda item: item[1], reverse=True)}
df_pie = pd.DataFrame({'item and channel': list(sorted_x.keys()), 'contribution': list(sorted_x.values())}, index=list(sorted_x.keys()))
df_pie | code |
128023684/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df_third.groupby(['cust_id', 'prod_cat_code', 'prod_subcat_code'])['transaction_id'].count().to_dict()
df_combined = pd.merge(df_third, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
grp_df = df_combined.groupby(['cust_id', 'prod_cat', 'prod_subcat', 'tran_date'])['tran_date', 'prod_subcat'].count()
df_combined[['cust_id', 'prod_cat', 'prod_subcat', 'tran_date']].value_counts() | code |
128023684/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df_third.groupby(['cust_id', 'prod_cat_code', 'prod_subcat_code'])['transaction_id'].count().to_dict()
df_combined = pd.merge(df_third, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
grp_df = df_combined.groupby(['cust_id', 'prod_cat', 'prod_subcat', 'tran_date'])['tran_date', 'prod_subcat'].count()
df_third.columns
df_third_part = df_third[df_third.Rate > 0].reset_index()
df_combined = pd.merge(df_third_part, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
combined_pair = df_combined[['prod_cat', 'Store_type']].drop_duplicates().to_dict('records')
contribution_dictionary = {}
for j in range(len(combined_pair)):
df_sub_part = df_combined[(df_combined.prod_cat == combined_pair[j]['prod_cat']) & (df_combined.Store_type == combined_pair[j]['Store_type'])].reset_index()
contribution_dictionary[combined_pair[j]['prod_cat'], combined_pair[j]['Store_type']] = np.round(100 * df_sub_part['total_amt'].sum() / df_combined['total_amt'].sum(), 2)
contribution_dictionary | code |
128023684/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128023684/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df_third.groupby(['cust_id', 'prod_cat_code', 'prod_subcat_code'])['transaction_id'].count().to_dict()
df_combined = pd.merge(df_third, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
grp_df = df_combined.groupby(['cust_id', 'prod_cat', 'prod_subcat', 'tran_date'])['tran_date', 'prod_subcat'].count()
df_third.columns
df_third_part = df_third[df_third.Rate > 0].reset_index()
df_combined = pd.merge(df_third_part, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
md = pd.DataFrame(df_combined[['cust_id', 'prod_cat', 'prod_subcat', 'tran_date']].value_counts())
combined_pair = df_combined[['prod_cat', 'Store_type']].drop_duplicates().to_dict('records')
contribution_dictionary = {}
for j in range(len(combined_pair)):
df_sub_part = df_combined[(df_combined.prod_cat == combined_pair[j]['prod_cat']) & (df_combined.Store_type == combined_pair[j]['Store_type'])].reset_index()
contribution_dictionary[combined_pair[j]['prod_cat'], combined_pair[j]['Store_type']] = np.round(100 * df_sub_part['total_amt'].sum() / df_combined['total_amt'].sum(), 2)
import operator
sorted_x = {k: v for k, v in sorted(contribution_dictionary.items(), key=lambda item: item[1], reverse=True)}
df_pie = pd.DataFrame({'item and channel': list(sorted_x.keys()), 'contribution': list(sorted_x.values())}, index=list(sorted_x.keys()))
channel_and_product_wise = {}
for i in range(df_pie.shape[0]):
df_part = df_combined[(df_combined.prod_cat == str(df_pie[['item and channel']].values.tolist()[i][0][0])) & (df_combined.Store_type == str(df_pie[['item and channel']].values.tolist()[i][0][1]))]
channel_and_product_wise[df_pie[['item and channel']].values.tolist()[i][0][0] + ',' + df_pie[['item and channel']].values.tolist()[i][0][1]] = np.round(df_part.groupby('cust_id')['total_amt'].sum(), 2).nlargest(100).to_dict()
df_second.keys()
df_second.sample(10)
user_lists = []
for key, values in channel_and_product_wise.items():
user_lists = list(values.keys())
df_second_part = df_second[df_second.customer_Id.isin(user_lists)].reset_index()
print(df_second_part.age.value_counts()) | code |
128023684/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df_third.groupby(['cust_id', 'prod_cat_code', 'prod_subcat_code'])['transaction_id'].count().to_dict()
df_combined = pd.merge(df_third, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
grp_df = df_combined.groupby(['cust_id', 'prod_cat', 'prod_subcat', 'tran_date'])['tran_date', 'prod_subcat'].count()
df_third.columns
df_third_part = df_third[df_third.Rate > 0].reset_index()
df_combined = pd.merge(df_third_part, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
md = pd.DataFrame(df_combined[['cust_id', 'prod_cat', 'prod_subcat', 'tran_date']].value_counts())
combined_pair = df_combined[['prod_cat', 'Store_type']].drop_duplicates().to_dict('records')
contribution_dictionary = {}
for j in range(len(combined_pair)):
df_sub_part = df_combined[(df_combined.prod_cat == combined_pair[j]['prod_cat']) & (df_combined.Store_type == combined_pair[j]['Store_type'])].reset_index()
contribution_dictionary[combined_pair[j]['prod_cat'], combined_pair[j]['Store_type']] = np.round(100 * df_sub_part['total_amt'].sum() / df_combined['total_amt'].sum(), 2)
import operator
sorted_x = {k: v for k, v in sorted(contribution_dictionary.items(), key=lambda item: item[1], reverse=True)}
df_pie = pd.DataFrame({'item and channel': list(sorted_x.keys()), 'contribution': list(sorted_x.values())}, index=list(sorted_x.keys()))
channel_and_product_wise = {}
for i in range(df_pie.shape[0]):
print(df_pie[['item and channel']].values.tolist()[i][0][0], '&', df_pie[['item and channel']].values.tolist()[i][0][1])
df_part = df_combined[(df_combined.prod_cat == str(df_pie[['item and channel']].values.tolist()[i][0][0])) & (df_combined.Store_type == str(df_pie[['item and channel']].values.tolist()[i][0][1]))]
channel_and_product_wise[df_pie[['item and channel']].values.tolist()[i][0][0] + ',' + df_pie[['item and channel']].values.tolist()[i][0][1]] = np.round(df_part.groupby('cust_id')['total_amt'].sum(), 2).nlargest(100).to_dict() | code |
128023684/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df_third.groupby(['cust_id', 'prod_cat_code', 'prod_subcat_code'])['transaction_id'].count().to_dict()
df_combined = pd.merge(df_third, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
grp_df = df_combined.groupby(['cust_id', 'prod_cat', 'prod_subcat', 'tran_date'])['tran_date', 'prod_subcat'].count()
df_third.columns
df_third_part = df_third[df_third.Rate > 0].reset_index()
df_combined = pd.merge(df_third_part, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
cust_id = 272098
tran_date = '13-05-2013'
df_combined[(df_combined['cust_id'] == cust_id) & (df_combined['tran_date'] == tran_date)] | code |
128023684/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_second.keys()
df_second.sample(10) | code |
128023684/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df_third.groupby(['cust_id', 'prod_cat_code', 'prod_subcat_code'])['transaction_id'].count().to_dict()
df_third.columns
df_third[(df_third['cust_id'] == 268663) & (df_third['tran_date'] == '22-04-2012')] | code |
128023684/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df_third.groupby(['cust_id', 'prod_cat_code', 'prod_subcat_code'])['transaction_id'].count().to_dict()
df_combined = pd.merge(df_third, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
grp_df = df_combined.groupby(['cust_id', 'prod_cat', 'prod_subcat', 'tran_date'])['tran_date', 'prod_subcat'].count() | code |
128023684/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df_third.groupby(['cust_id', 'prod_cat_code', 'prod_subcat_code'])['transaction_id'].count().to_dict()
df_combined = pd.merge(df_third, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
grp_df = df_combined.groupby(['cust_id', 'prod_cat', 'prod_subcat', 'tran_date'])['tran_date', 'prod_subcat'].count()
df_third.columns
df_third_part = df_third[df_third.Rate > 0].reset_index()
df_combined = pd.merge(df_third_part, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
combined_pair = df_combined[['prod_cat', 'Store_type']].drop_duplicates().to_dict('records')
contribution_dictionary = {}
for j in range(len(combined_pair)):
df_sub_part = df_combined[(df_combined.prod_cat == combined_pair[j]['prod_cat']) & (df_combined.Store_type == combined_pair[j]['Store_type'])].reset_index()
contribution_dictionary[combined_pair[j]['prod_cat'], combined_pair[j]['Store_type']] = np.round(100 * df_sub_part['total_amt'].sum() / df_combined['total_amt'].sum(), 2)
df_combined.head(5) | code |
128023684/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df_third.groupby(['cust_id', 'prod_cat_code', 'prod_subcat_code'])['transaction_id'].count().to_dict()
df_combined = pd.merge(df_third, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
grp_df = df_combined.groupby(['cust_id', 'prod_cat', 'prod_subcat', 'tran_date'])['tran_date', 'prod_subcat'].count()
df_combined[(df_combined['cust_id'] == 268663) & (df_combined['tran_date'] == '22-04-2012')] | code |
128023684/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
df_third.head(10) | code |
129012029/cell_21 | [
"text_html_output_1.png"
] | from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
from matplotlib.pyplot import figure
df = yf.download(ticker, start='2020-05-10', end='2021-05-10')
from matplotlib.pyplot import figure
figure(figsize=(15, 7), dpi=80)
plt.plot(df.Close)
plt.show() | code |
129012029/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
tesla.head() | code |
129012029/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
from matplotlib.pyplot import figure
df = yf.download(ticker, start='2020-05-10', end='2021-05-10')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='1d')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='5d')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='1mo')
from matplotlib.pyplot import figure
figure(figsize=(15, 7), dpi=80)
plt.plot(df.Close)
plt.show() | code |
129012029/cell_23 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
from matplotlib.pyplot import figure
df = yf.download(ticker, start='2020-05-10', end='2021-05-10')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='1d')
from matplotlib.pyplot import figure
figure(figsize=(15, 7), dpi=80)
plt.plot(df.Close)
plt.show() | code |
129012029/cell_20 | [
"text_plain_output_1.png"
] | import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
df = yf.download(ticker, start='2020-05-10', end='2021-05-10')
df.info() | code |
129012029/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
from matplotlib.pyplot import figure
df = yf.download(ticker, start='2020-05-10', end='2021-05-10')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='1d')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='5d')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='1mo')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='6mo')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='1y')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='1d', interval='1m')
from matplotlib.pyplot import figure
figure(figsize=(15, 7), dpi=80)
plt.plot(df.Close)
plt.show() | code |
129012029/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
from matplotlib.pyplot import figure
df = yf.download(ticker, start='2020-05-10', end='2021-05-10')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='1d')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='5d')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='1mo')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='6mo')
from matplotlib.pyplot import figure
figure(figsize=(15, 7), dpi=80)
plt.plot(df.Close)
plt.show() | code |
129012029/cell_2 | [
"image_output_1.png"
] | !pip install yfinance | code |
129012029/cell_11 | [
"text_plain_output_1.png"
] | import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
tesla.describe() | code |
129012029/cell_19 | [
"text_html_output_1.png"
] | import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
df = yf.download(ticker, start='2020-05-10', end='2021-05-10')
df | code |
129012029/cell_18 | [
"text_plain_output_1.png"
] | import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
df = yf.download(ticker, start='2020-05-10', end='2021-05-10') | code |
129012029/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker) | code |
129012029/cell_24 | [
"image_output_1.png"
] | from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
from matplotlib.pyplot import figure
df = yf.download(ticker, start='2020-05-10', end='2021-05-10')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='1d')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='5d')
from matplotlib.pyplot import figure
figure(figsize=(15, 7), dpi=80)
plt.plot(df.Close)
plt.show() | code |
129012029/cell_14 | [
"text_html_output_1.png"
] | from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
from matplotlib.pyplot import figure
figure(figsize=(15, 7), dpi=80)
plt.plot(tesla.Close)
plt.show() | code |
129012029/cell_10 | [
"text_plain_output_1.png"
] | import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
tesla.info() | code |
129012029/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
from matplotlib.pyplot import figure
df = yf.download(ticker, start='2020-05-10', end='2021-05-10')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='1d')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='5d')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='1mo')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='6mo')
from matplotlib.pyplot import figure
df = yf.download(ticker, period='1y')
from matplotlib.pyplot import figure
figure(figsize=(15, 7), dpi=80)
plt.plot(df.Close)
plt.show() | code |
2017020/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from pandas import DataFrame
from pandas import Series
import matplotlib.pyplot as plt
data = pd.read_csv('../input/Top_hashtag.csv')
data.shape | code |
2017020/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2017020/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
left = [1, 2, 3, 4, 5]
height = [4967, 6833, 893, 813, 3473]
tick_label = ['love', 'freind', 'beachfamily', 'yellow']
plt.bar(left, height, tick_label=tick_label, width=0.8, color=['blue', 'blue'])
plt.xlabel('x - axis')
plt.ylabel('y - axis')
plt.title('My bar chart!')
plt.show() | code |
105187552/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/penguins/penguins.csv')
'In order to take a closer look at our dataset, we will use head() to print\nthe first five observations of our dataset and tail() to print the last five observations.'
"""Now we will find the total no. of rows and columns in
the dataset using .shape"""
df.shape
"""Checking the data-types of the columns, along with
finding whether they contain null values or not."""
"""describe() function provides us with statistical summary.
This function returns the count,mean,standard deviation,minimum and maximum values
and the quantiles of the data."""
df.describe() | code |
105187552/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/penguins/penguins.csv')
'In order to take a closer look at our dataset, we will use head() to print\nthe first five observations of our dataset and tail() to print the last five observations.'
df.tail() | code |
105187552/cell_6 | [
"text_plain_output_1.png"
] | """Here, we conclude that our dataset comprises of
344 observations and 9 characteristics.""" | code |
105187552/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
105187552/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/penguins/penguins.csv')
'In order to take a closer look at our dataset, we will use head() to print\nthe first five observations of our dataset and tail() to print the last five observations.'
"""Now we will find the total no. of rows and columns in
the dataset using .shape"""
df.shape
"""Checking the data-types of the columns, along with
finding whether they contain null values or not."""
df.info() | code |
105187552/cell_8 | [
"text_html_output_1.png"
] | """So the conclusion is that our data contains float, integer and string values(object).
Also,there is no null/missing values. """ | code |
105187552/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/penguins/penguins.csv')
'In order to take a closer look at our dataset, we will use head() to print\nthe first five observations of our dataset and tail() to print the last five observations.'
df.head() | code |
105187552/cell_10 | [
"text_html_output_1.png"
] | import seaborn as sns
data = sns.load_dataset('penguins') | code |
105187552/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/penguins/penguins.csv')
'In order to take a closer look at our dataset, we will use head() to print\nthe first five observations of our dataset and tail() to print the last five observations.'
"""Now we will find the total no. of rows and columns in
the dataset using .shape"""
df.shape | code |
89130324/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
train_data.shape
train_data.columns | code |
89130324/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf = clf.fit(X_train, y_train)
clf.score(X_train, y_train) | code |
89130324/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
train_data.shape
test_data.shape
train_data.columns
train_data.isnull().sum()
test_data.isnull().sum()
drop_cols = ['id', 'obj_ID', 'run_ID', 'MJD']
train_data.drop(columns=drop_cols, inplace=True)
submission_df = pd.DataFrame({'id': test_data.id})
test_data.drop(columns=drop_cols, inplace=True)
sns.countplot(submission_df.label) | code |
89130324/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
train_data.shape
train_data.columns
train_data.isnull().sum() | code |
89130324/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89130324/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
train_data.shape | code |
89130324/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
train_data.shape
test_data.shape
train_data.columns
train_data.isnull().sum()
test_data.isnull().sum()
drop_cols = ['id', 'obj_ID', 'run_ID', 'MJD']
train_data.drop(columns=drop_cols, inplace=True)
submission_df = pd.DataFrame({'id': test_data.id})
test_data.drop(columns=drop_cols, inplace=True)
y = train_data['label']
X = train_data.drop(columns=['label'])
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=18)
print(len(X_train), len(X_test)) | code |
89130324/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
test_data.shape | code |
89130324/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
train_data.shape
test_data.shape
train_data.columns
train_data.isnull().sum()
test_data.isnull().sum()
drop_cols = ['id', 'obj_ID', 'run_ID', 'MJD']
train_data.drop(columns=drop_cols, inplace=True)
submission_df = pd.DataFrame({'id': test_data.id})
test_data.drop(columns=drop_cols, inplace=True)
sns.heatmap(train_data.corr(), annot=True) | code |
89130324/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf = clf.fit(X_train, y_train)
clf.score(X_train, y_train)
clf.score(X_test, y_test) | code |
89130324/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
train_data.shape
train_data.columns
train_data.head() | code |
89130324/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
test_data.shape
test_data.isnull().sum() | code |
128017503/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age'], ax = axes[0][0])
sns.boxplot(y = train['Height'], ax = axes[0][1])
sns.boxplot(y = train['Weight'], ax = axes[0][2])
sns.boxplot(y = train['Duration'], ax = axes[1][0])
sns.boxplot(y = train['Heart_Rate'], ax = axes[1][1])
sns.boxplot(y = train['Body_Temp'],ax = axes[1][2])
plt.tight_layout()
plt.show()
plt.figure(figsize=(10, 10))
mask = np.zeros_like(train.corr())
mask[np.triu_indices_from(mask)] = True
sns.heatmap(train.corr(), mask=mask, annot=True, cmap='Blues')
plt.show() | code |
128017503/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
exercise = pd.read_csv('/kaggle/input/fmendesdat263xdemos/exercise.csv')
calories = pd.read_csv('/kaggle/input/fmendesdat263xdemos/calories.csv')
exercise['Calories_Burned'] = calories['Calories']
exercise = exercise.drop(['User_ID'], axis=1)
exercise | code |
128017503/cell_2 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import warnings
import pandas as pd
import numpy as np
import random
import os
import gc
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, Ridge
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore') | code |
128017503/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
fig, axes = plt.subplots(2, 3, figsize=(10, 10))
sns.boxplot(y=train['Age'], ax=axes[0][0])
sns.boxplot(y=train['Height'], ax=axes[0][1])
sns.boxplot(y=train['Weight'], ax=axes[0][2])
sns.boxplot(y=train['Duration'], ax=axes[1][0])
sns.boxplot(y=train['Heart_Rate'], ax=axes[1][1])
sns.boxplot(y=train['Body_Temp'], ax=axes[1][2])
plt.tight_layout()
plt.show() | code |
128017503/cell_19 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age'], ax = axes[0][0])
sns.boxplot(y = train['Height'], ax = axes[0][1])
sns.boxplot(y = train['Weight'], ax = axes[0][2])
sns.boxplot(y = train['Duration'], ax = axes[1][0])
sns.boxplot(y = train['Heart_Rate'], ax = axes[1][1])
sns.boxplot(y = train['Body_Temp'],ax = axes[1][2])
plt.tight_layout()
plt.show()
mask = np.zeros_like(train.corr())
mask[np.triu_indices_from(mask)] = True
plt.figure(figsize=(5, 5))
plt.pie(train.Gender.value_counts(), labels=train.Gender.value_counts().index, autopct='%.2f%%')
plt.legend()
plt.show() | code |
128017503/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age'], ax = axes[0][0])
sns.boxplot(y = train['Height'], ax = axes[0][1])
sns.boxplot(y = train['Weight'], ax = axes[0][2])
sns.boxplot(y = train['Duration'], ax = axes[1][0])
sns.boxplot(y = train['Heart_Rate'], ax = axes[1][1])
sns.boxplot(y = train['Body_Temp'],ax = axes[1][2])
plt.tight_layout()
plt.show()
mask = np.zeros_like(train.corr())
mask[np.triu_indices_from(mask)] = True
plt.plot(train['Age'], train['Calories_Burned'], 'g*')
plt.title('Age vs Calories Burned')
plt.xlabel('Age')
plt.ylabel('Calories Burned')
plt.show()
plt.plot(train['Height'], train['Calories_Burned'], 'g*')
plt.title('Height vs Calories Burned')
plt.xlabel('Height')
plt.ylabel('Calories Burned')
plt.show()
plt.plot(train['Weight'], train['Calories_Burned'], 'g*')
plt.title('Weight vs Calories Burned')
plt.xlabel('Weight')
plt.ylabel('Calories Burned')
plt.show() | code |
128017503/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age'], ax = axes[0][0])
sns.boxplot(y = train['Height'], ax = axes[0][1])
sns.boxplot(y = train['Weight'], ax = axes[0][2])
sns.boxplot(y = train['Duration'], ax = axes[1][0])
sns.boxplot(y = train['Heart_Rate'], ax = axes[1][1])
sns.boxplot(y = train['Body_Temp'],ax = axes[1][2])
plt.tight_layout()
plt.show()
mask = np.zeros_like(train.corr())
mask[np.triu_indices_from(mask)] = True
plt.plot(train['Heart_Rate'], train['Calories_Burned'], 'go')
plt.title('Heart_Rate vs Calories Burned')
plt.xlabel('Heart_Rate')
plt.ylabel('Calories Burned')
plt.show()
plt.plot(train['Body_Temp'], train['Calories_Burned'], 'go')
plt.title('Body Temp vs Calories Burned')
plt.xlabel('Body Temp')
plt.ylabel('Calories Burned')
plt.show()
plt.plot(train['Duration'], train['Calories_Burned'], 'go')
plt.title('Duration vs Calories Burned')
plt.xlabel('Duration')
plt.ylabel('Calories Burned')
plt.show() | code |
128017503/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age'], ax = axes[0][0])
sns.boxplot(y = train['Height'], ax = axes[0][1])
sns.boxplot(y = train['Weight'], ax = axes[0][2])
sns.boxplot(y = train['Duration'], ax = axes[1][0])
sns.boxplot(y = train['Heart_Rate'], ax = axes[1][1])
sns.boxplot(y = train['Body_Temp'],ax = axes[1][2])
plt.tight_layout()
plt.show()
mask = np.zeros_like(train.corr())
mask[np.triu_indices_from(mask)] = True
plt.figure(figsize=(10, 10))
sns.heatmap(train.corr(), annot=True, cmap='YlOrRd')
plt.show() | code |
105196788/cell_21 | [
"text_html_output_1.png"
] | from sklearn.linear_model import Ridge, RidgeCV
import numpy as np
import numpy as np # linear algebra
from sklearn.linear_model import Ridge, RidgeCV
alphas = np.random.uniform(0, 10, 50)
ridge_cv = RidgeCV(alphas=alphas, cv=10, normalize=True)
ridge_cv.fit(X_train, y_train)
alpha = ridge_cv.alpha_
alpha
ridge = Ridge(alpha=ridge_cv.alpha_)
ridge.fit(X_train, y_train) | code |
105196788/cell_13 | [
"image_output_1.png"
] | from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = data.target
X = df.drop(columns='MEDV', axis=1)
y = df['MEDV']
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
from statsmodels.stats.outliers_influence import variance_inflation_factor
vif = pd.DataFrame()
vif['VIF'] = [variance_inflation_factor(X_scaled, i) for i in range(X_scaled.shape[1])]
vif['Features'] = X.columns
vif
X.drop(columns=['TAX'], axis=1) | code |
105196788/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
from sklearn.linear_model import Ridge, RidgeCV
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = data.target
X = df.drop(columns='MEDV', axis=1)
y = df['MEDV']
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
from statsmodels.stats.outliers_influence import variance_inflation_factor
vif = pd.DataFrame()
vif['VIF'] = [variance_inflation_factor(X_scaled, i) for i in range(X_scaled.shape[1])]
vif['Features'] = X.columns
vif
X.drop(columns=['TAX'], axis=1)
from sklearn.linear_model import Ridge, RidgeCV
alphas = np.random.uniform(0, 10, 50)
ridge_cv = RidgeCV(alphas=alphas, cv=10, normalize=True)
ridge_cv.fit(X_train, y_train)
alpha = ridge_cv.alpha_
alpha
ridge = Ridge(alpha=ridge_cv.alpha_)
ridge.fit(X_train, y_train)
ridge.score(X_train, y_train)
ridge.score(X_test, y_test)
def adj_r2(X, y, model):
r2 = model.score(X, y)
n = X.shape[0]
p = X.shape[1]
adjusted_r2 = 1 - (1 - r2) * (n - 1) / (n - p - 1)
return adjusted_r2
print(adj_r2(X_train, y_train, ridge)) | code |
105196788/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
print(df) | code |
105196788/cell_23 | [
"text_html_output_1.png"
] | from sklearn.linear_model import Ridge, RidgeCV
import numpy as np
import numpy as np # linear algebra
from sklearn.linear_model import Ridge, RidgeCV
alphas = np.random.uniform(0, 10, 50)
ridge_cv = RidgeCV(alphas=alphas, cv=10, normalize=True)
ridge_cv.fit(X_train, y_train)
alpha = ridge_cv.alpha_
alpha
ridge = Ridge(alpha=ridge_cv.alpha_)
ridge.fit(X_train, y_train)
ridge.score(X_train, y_train)
ridge.score(X_test, y_test) | code |
105196788/cell_20 | [
"image_output_1.png"
] | from sklearn.linear_model import Ridge, RidgeCV
import numpy as np
import numpy as np # linear algebra
from sklearn.linear_model import Ridge, RidgeCV
alphas = np.random.uniform(0, 10, 50)
ridge_cv = RidgeCV(alphas=alphas, cv=10, normalize=True)
ridge_cv.fit(X_train, y_train)
alpha = ridge_cv.alpha_
alpha | code |
105196788/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = data.target
plt.figure(figsize=(20, 15))
plotnumber = 1
for column in df:
if plotnumber <= 14:
ax = plt.subplot(3, 5, plotnumber)
sns.distplot(df[column])
plt.xlabel(column, fontsize=15)
plotnumber += 1
plt.tight_layout()
plt.show() | code |
105196788/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
from sklearn.linear_model import Ridge, RidgeCV
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = data.target
X = df.drop(columns='MEDV', axis=1)
y = df['MEDV']
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
from statsmodels.stats.outliers_influence import variance_inflation_factor
vif = pd.DataFrame()
vif['VIF'] = [variance_inflation_factor(X_scaled, i) for i in range(X_scaled.shape[1])]
vif['Features'] = X.columns
vif
X.drop(columns=['TAX'], axis=1)
from sklearn.linear_model import Ridge, RidgeCV
alphas = np.random.uniform(0, 10, 50)
ridge_cv = RidgeCV(alphas=alphas, cv=10, normalize=True)
ridge_cv.fit(X_train, y_train)
alpha = ridge_cv.alpha_
alpha
ridge = Ridge(alpha=ridge_cv.alpha_)
ridge.fit(X_train, y_train)
ridge.score(X_train, y_train)
ridge.score(X_test, y_test)
def adj_r2(X, y, model):
r2 = model.score(X, y)
n = X.shape[0]
p = X.shape[1]
adjusted_r2 = 1 - (1 - r2) * (n - 1) / (n - p - 1)
return adjusted_r2
print(adj_r2(X_train, y_train, ridge)) | code |
105196788/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = data.target
X = df.drop(columns='MEDV', axis=1)
y = df['MEDV']
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
X_scaled | code |
105196788/cell_19 | [
"text_html_output_1.png"
] | from sklearn.linear_model import Ridge, RidgeCV
import numpy as np
import numpy as np # linear algebra
from sklearn.linear_model import Ridge, RidgeCV
alphas = np.random.uniform(0, 10, 50)
ridge_cv = RidgeCV(alphas=alphas, cv=10, normalize=True)
ridge_cv.fit(X_train, y_train) | code |
105196788/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
105196788/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = data.target
plt.figure(figsize = (20, 15))
plotnumber = 1
for column in df:
if plotnumber <= 14:
ax = plt.subplot(3, 5, plotnumber)
sns.distplot(df[column])
plt.xlabel(column, fontsize = 15)
plotnumber += 1
plt.tight_layout()
plt.show()
plt.figure(figsize=(20, 15))
plotnumber = 1
for column in df:
if plotnumber <= 14:
ax = plt.subplot(3, 5, plotnumber)
sns.scatterplot(x=df['MEDV'], y=df[column])
plotnumber += 1
plt.tight_layout()
plt.show() | code |
105196788/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = data.target
plt.figure(figsize = (20, 15))
plotnumber = 1
for column in df:
if plotnumber <= 14:
ax = plt.subplot(3, 5, plotnumber)
sns.distplot(df[column])
plt.xlabel(column, fontsize = 15)
plotnumber += 1
plt.tight_layout()
plt.show()
plt.figure(figsize = (20, 15))
plotnumber = 1
for column in df:
if plotnumber <= 14:
ax = plt.subplot(3, 5, plotnumber)
sns.scatterplot(x = df['MEDV'], y = df[column])
plotnumber += 1
plt.tight_layout()
plt.show()
plt.figure(figsize=(20, 8))
sns.boxplot(data=df, width=0.8)
plt.show() | code |
105196788/cell_15 | [
"image_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import statsmodels.formula.api as smf
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = data.target
plt.figure(figsize = (20, 15))
plotnumber = 1
for column in df:
if plotnumber <= 14:
ax = plt.subplot(3, 5, plotnumber)
sns.distplot(df[column])
plt.xlabel(column, fontsize = 15)
plotnumber += 1
plt.tight_layout()
plt.show()
plt.figure(figsize = (20, 15))
plotnumber = 1
for column in df:
if plotnumber <= 14:
ax = plt.subplot(3, 5, plotnumber)
sns.scatterplot(x = df['MEDV'], y = df[column])
plotnumber += 1
plt.tight_layout()
plt.show()
X = df.drop(columns='MEDV', axis=1)
y = df['MEDV']
# Heatmap
fig, ax = plt.subplots(figsize = (16, 8))
sns.heatmap(df.corr(), annot = True, fmt = '1.2f', annot_kws = {'size' : 10}, linewidth = 1)
plt.show()
import statsmodels.formula.api as smf
lm = smf.ols(formula='MEDV ~ RAD', data=df).fit()
lm.summary() | code |
105196788/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import statsmodels.formula.api as smf
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = data.target
plt.figure(figsize = (20, 15))
plotnumber = 1
for column in df:
if plotnumber <= 14:
ax = plt.subplot(3, 5, plotnumber)
sns.distplot(df[column])
plt.xlabel(column, fontsize = 15)
plotnumber += 1
plt.tight_layout()
plt.show()
plt.figure(figsize = (20, 15))
plotnumber = 1
for column in df:
if plotnumber <= 14:
ax = plt.subplot(3, 5, plotnumber)
sns.scatterplot(x = df['MEDV'], y = df[column])
plotnumber += 1
plt.tight_layout()
plt.show()
X = df.drop(columns='MEDV', axis=1)
y = df['MEDV']
# Heatmap
fig, ax = plt.subplots(figsize = (16, 8))
sns.heatmap(df.corr(), annot = True, fmt = '1.2f', annot_kws = {'size' : 10}, linewidth = 1)
plt.show()
import statsmodels.formula.api as smf
lm = smf.ols(formula='MEDV ~ RAD', data=df).fit()
lm.summary()
lm = smf.ols(formula='MEDV ~ TAX', data=df).fit()
lm.summary() | code |
105196788/cell_17 | [
"text_html_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = data.target
plt.figure(figsize = (20, 15))
plotnumber = 1
for column in df:
if plotnumber <= 14:
ax = plt.subplot(3, 5, plotnumber)
sns.distplot(df[column])
plt.xlabel(column, fontsize = 15)
plotnumber += 1
plt.tight_layout()
plt.show()
plt.figure(figsize = (20, 15))
plotnumber = 1
for column in df:
if plotnumber <= 14:
ax = plt.subplot(3, 5, plotnumber)
sns.scatterplot(x = df['MEDV'], y = df[column])
plotnumber += 1
plt.tight_layout()
plt.show()
X = df.drop(columns='MEDV', axis=1)
y = df['MEDV']
# Heatmap
fig, ax = plt.subplots(figsize = (16, 8))
sns.heatmap(df.corr(), annot = True, fmt = '1.2f', annot_kws = {'size' : 10}, linewidth = 1)
plt.show()
df.drop(columns='RAD', axis=1, inplace=True)
df.head() | code |
105196788/cell_14 | [
"image_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = data.target
plt.figure(figsize = (20, 15))
plotnumber = 1
for column in df:
if plotnumber <= 14:
ax = plt.subplot(3, 5, plotnumber)
sns.distplot(df[column])
plt.xlabel(column, fontsize = 15)
plotnumber += 1
plt.tight_layout()
plt.show()
plt.figure(figsize = (20, 15))
plotnumber = 1
for column in df:
if plotnumber <= 14:
ax = plt.subplot(3, 5, plotnumber)
sns.scatterplot(x = df['MEDV'], y = df[column])
plotnumber += 1
plt.tight_layout()
plt.show()
X = df.drop(columns='MEDV', axis=1)
y = df['MEDV']
fig, ax = plt.subplots(figsize=(16, 8))
sns.heatmap(df.corr(), annot=True, fmt='1.2f', annot_kws={'size': 10}, linewidth=1)
plt.show() | code |
105196788/cell_22 | [
"text_html_output_1.png"
] | from sklearn.linear_model import Ridge, RidgeCV
import numpy as np
import numpy as np # linear algebra
from sklearn.linear_model import Ridge, RidgeCV
alphas = np.random.uniform(0, 10, 50)
ridge_cv = RidgeCV(alphas=alphas, cv=10, normalize=True)
ridge_cv.fit(X_train, y_train)
alpha = ridge_cv.alpha_
alpha
ridge = Ridge(alpha=ridge_cv.alpha_)
ridge.fit(X_train, y_train)
ridge.score(X_train, y_train) | code |
105196788/cell_12 | [
"text_html_output_1.png"
] | from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = data.target
X = df.drop(columns='MEDV', axis=1)
y = df['MEDV']
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
from statsmodels.stats.outliers_influence import variance_inflation_factor
vif = pd.DataFrame()
vif['VIF'] = [variance_inflation_factor(X_scaled, i) for i in range(X_scaled.shape[1])]
vif['Features'] = X.columns
vif | code |
105196788/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = data.target
df.head() | code |
105210042/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
plt.figure(figsize=(12, 8))
sns.set_palette('bright')
plt.title('Car Price Plot')
sns.histplot(data['price'])
plt.show() | code |
105210042/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
data.head() | code |
105210042/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.info() | code |
105210042/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_absolute_error , r2_score , mean_squared_error
import numpy as np
from sklearn.linear_model import LinearRegression
Lr = LinearRegression()
Lr.fit(X_train, y_train)
Lr.score(X_test, y_test)
from sklearn.linear_model import Lasso
LO = Lasso()
LO.fit(X_train, y_train)
LO.score(X_test, y_test)
from sklearn.linear_model import Ridge
RD = Ridge()
RD.fit(X_train, y_train)
RD.score(X_test, y_test)
print('Test RMSE', np.sqrt(mean_squared_error(y_test, RD.predict(X_test))))
print('Train RMSE', np.sqrt(mean_squared_error(y_train, RD.predict(X_train))))
print('R2 Test', r2_score(y_test, RD.predict(X_test))) | code |
105210042/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
data.duplicated().sum() | code |
105210042/cell_30 | [
"image_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.linear_model import Lasso
LO = Lasso()
LO.fit(X_train, y_train)
LO.score(X_test, y_test) | code |
105210042/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Ridge
from sklearn.linear_model import Ridge
RD = Ridge()
RD.fit(X_train, y_train)
RD.score(X_test, y_test) | code |
105210042/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
plt.figure(figsize=(15, 7))
sns.heatmap(data.corr(), annot=True, cmap='Blues')
plt.title('Data Correlation', size=15)
plt.ylabel('Columns', size=15)
plt.xlabel('Columns', size=15)
plt.show() | code |
105210042/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
data.describe() | code |
105210042/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
sns.pairplot(data[['horsepower', 'price', 'symboling']], hue='symboling') | code |
105210042/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
data.describe(include=['O']) | code |
105210042/cell_18 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
sns.scatterplot(x='horsepower', y='price', data=data, color='b') | code |
105210042/cell_28 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error , r2_score , mean_squared_error
import numpy as np
from sklearn.linear_model import LinearRegression
Lr = LinearRegression()
Lr.fit(X_train, y_train)
Lr.score(X_test, y_test)
print('Test RMSE', np.sqrt(mean_squared_error(y_test, Lr.predict(X_test))))
print('Train RMSE', np.sqrt(mean_squared_error(y_train, Lr.predict(X_train))))
print('R2 Test', r2_score(y_test, Lr.predict(X_test))) | code |
105210042/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
sns.barplot(x='symboling', y='count', data=df_v) | code |
105210042/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
sns.boxplot(x='symboling', y='price', data=data, palette='Pastel1') | code |
105210042/cell_3 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.head(5) | code |
105210042/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
sns.scatterplot(x='wheelbase', y='price', data=data, color='purple') | code |
105210042/cell_31 | [
"image_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error , r2_score , mean_squared_error
import numpy as np
from sklearn.linear_model import LinearRegression
Lr = LinearRegression()
Lr.fit(X_train, y_train)
Lr.score(X_test, y_test)
from sklearn.linear_model import Lasso
LO = Lasso()
LO.fit(X_train, y_train)
LO.score(X_test, y_test)
print('Test RMSE', np.sqrt(mean_squared_error(y_test, LO.predict(X_test))))
print('Train RMSE', np.sqrt(mean_squared_error(y_train, LO.predict(X_train))))
print('R2 Test', r2_score(y_test, LO.predict(X_test))) | code |
105210042/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
sns.boxplot(x='symboling', y='price', data=data, palette='winter_r') | code |
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