path
stringlengths 13
17
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sequencelengths 1
873
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stringlengths 0
40.4k
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stringclasses 1
value |
|---|---|---|---|
106210927/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
df_train.info() | code |
106210927/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
df_train.head(3) | code |
106210927/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
df_train['sample'] = 1
df_test['sample'] = 0
df_test['reviewer_score'] = 0
hotels = pd.concat([df_train, df_test], ignore_index=True)
plt.rcParams['figure.figsize'] = (15, 10)
sns.heatmap(hotels.drop(['sample'], axis=1).corr(), annot=True) | code |
106210927/cell_3 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import collections
import re
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
nltk.downloader.download('vader_lexicon')
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from sklearn.model_selection import train_test_split
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106210927/cell_10 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
df_test.head(3) | code |
106210927/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
sample_submission.info() | code |
331819/cell_4 | [
"image_output_2.png",
"image_output_1.png"
] | qplot(SepalLengthCm, SepalWidthCm, data=df, col=Species)
qplot(PetalLengthCm, PetalWidthCm, data=df, col=Species) | code |
331819/cell_2 | [
"text_html_output_1.png"
] | library(ggplot2)
library(readr)
df < -read_csv('../input/Iris.csv')
head(df) | code |
331819/cell_3 | [
"image_output_2.png",
"image_output_1.png"
] | qplot(SepalLengthCm, SepalWidthCm, data=df)
qplot(PetalLengthCm, PetalWidthCm, data=df) | code |
105202366/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data
ds_data.isnull().sum()
ds_data.head() | code |
105202366/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data
ds_data.isnull().sum() | code |
105202366/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data | code |
105202366/cell_23 | [
"text_plain_output_1.png"
] | len(x_test) | code |
105202366/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.naive_bayes import MultinomialNB
naive = MultinomialNB()
naive.fit(x_train, y_train)
naive_score = naive.score(x_test, y_test)
naive_score | code |
105202366/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data
ds_data.info() | code |
105202366/cell_1 | [
"text_plain_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
105202366/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data
len(ds_data) | code |
105202366/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.naive_bayes import MultinomialNB
naive = MultinomialNB()
naive.fit(x_train, y_train) | code |
105202366/cell_28 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
log = LogisticRegression()
log.fit(x_train, y_train)
log_score = log.score(x_test, y_test)
log_score | code |
105202366/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data
ds_data.isnull().sum()
sns.heatmap(ds_data.corr()) | code |
105202366/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data.head() | code |
105202366/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
import matplotlib.pyplot as plt
log = LogisticRegression()
log.fit(x_train, y_train)
log_score = log.score(x_test, y_test)
log_score
naive = MultinomialNB()
naive.fit(x_train, y_train)
naive_score = naive.score(x_test, y_test)
naive_score
plt.hist(log_score, color='Red')
plt.hist(naive_score, color='Green')
plt.title('Logistic vs Naive Bayes Algorithm')
plt.show() | code |
105202366/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data
ds_data.isnull().sum()
ds_data.info() | code |
105202366/cell_22 | [
"text_plain_output_1.png"
] | len(x_train) | code |
105202366/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
log = LogisticRegression()
log.fit(x_train, y_train) | code |
105202366/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data
ds_data.describe() | code |
2015269/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
x_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(x_columns):
if data_df[c].nunique() == 1:
single_val_c[c] = data_df[c].unique()[0]
continue
gb = data_df.groupby(c)
m = gb['y'].mean()
s = gb.size()
df = pd.DataFrame(index=range(len(m)))
df['col'] = c
df['val'] = m.index.values
df['positive_persent'] = m.values
df['count_persent'] = s.values / s.sum()
stats_df.append(df)
trace_prate = go.Bar(x=df['val'], y=df['positive_persent'] * 100, name='positive persent')
trace_cnt = go.Bar(x=df['val'], y=df['count_persent'] * 100, name='count persent')
layout = go.Layout(xaxis=dict(title=c), yaxis=dict(title='positive and count persent'))
fig = go.Figure(data=[trace_prate, trace_cnt], layout=layout)
stats_df = pd.concat(stats_df, axis=0)
for c in single_val_c.keys():
print('The column %s only has one unique value with %r.' % (c, single_val_c[c]))
print('It does work for the classification, which will be removed.')
x_columns.remove(c) | code |
2015269/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('../input/mushrooms.csv')
data_df.info() | code |
2015269/cell_6 | [
"text_html_output_10.png",
"text_html_output_16.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_15.png",
"text_html_output_5.png",
"text_html_output_14.png",
"text_html_output_19.png",
"text_html_output_9.png",
"text_html_output_13.png",
"text_html_output_20.png",
"text_html_output_21.png",
"text_html_output_1.png",
"text_html_output_17.png",
"text_html_output_18.png",
"text_html_output_12.png",
"text_html_output_11.png",
"text_html_output_8.png",
"text_html_output_3.png",
"text_html_output_7.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
x_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(x_columns):
if data_df[c].nunique() == 1:
single_val_c[c] = data_df[c].unique()[0]
continue
gb = data_df.groupby(c)
m = gb['y'].mean()
s = gb.size()
df = pd.DataFrame(index=range(len(m)))
df['col'] = c
df['val'] = m.index.values
df['positive_persent'] = m.values
df['count_persent'] = s.values / s.sum()
stats_df.append(df)
trace_prate = go.Bar(x=df['val'], y=df['positive_persent'] * 100, name='positive persent')
trace_cnt = go.Bar(x=df['val'], y=df['count_persent'] * 100, name='count persent')
layout = go.Layout(xaxis=dict(title=c), yaxis=dict(title='positive and count persent'))
fig = go.Figure(data=[trace_prate, trace_cnt], layout=layout)
pyo.iplot(fig)
stats_df = pd.concat(stats_df, axis=0) | code |
2015269/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
x_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(x_columns):
if data_df[c].nunique() == 1:
single_val_c[c] = data_df[c].unique()[0]
continue
gb = data_df.groupby(c)
m = gb['y'].mean()
s = gb.size()
df = pd.DataFrame(index=range(len(m)))
df['col'] = c
df['val'] = m.index.values
df['positive_persent'] = m.values
df['count_persent'] = s.values / s.sum()
stats_df.append(df)
trace_prate = go.Bar(x=df['val'], y=df['positive_persent'] * 100, name='positive persent')
trace_cnt = go.Bar(x=df['val'], y=df['count_persent'] * 100, name='count persent')
layout = go.Layout(xaxis=dict(title=c), yaxis=dict(title='positive and count persent'))
fig = go.Figure(data=[trace_prate, trace_cnt], layout=layout)
stats_df = pd.concat(stats_df, axis=0)
stats_df.describe() | code |
2015269/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
x_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(x_columns):
if data_df[c].nunique() == 1:
single_val_c[c] = data_df[c].unique()[0]
continue
gb = data_df.groupby(c)
m = gb['y'].mean()
s = gb.size()
df = pd.DataFrame(index=range(len(m)))
df['col'] = c
df['val'] = m.index.values
df['positive_persent'] = m.values
df['count_persent'] = s.values / s.sum()
stats_df.append(df)
trace_prate = go.Bar(x=df['val'], y=df['positive_persent'] * 100, name='positive persent')
trace_cnt = go.Bar(x=df['val'], y=df['count_persent'] * 100, name='count persent')
layout = go.Layout(xaxis=dict(title=c), yaxis=dict(title='positive and count persent'))
fig = go.Figure(data=[trace_prate, trace_cnt], layout=layout)
stats_df = pd.concat(stats_df, axis=0)
stats_df.head(10) | code |
2015269/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
np.set_printoptions(suppress=True, linewidth=300)
pd.options.display.float_format = lambda x: '%0.6f' % x
pyo.init_notebook_mode(connected=True)
print(check_output(['ls', '../input']).decode('utf-8')) | code |
2015269/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('../input/mushrooms.csv')
data_df.head() | code |
104115759/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum() | code |
104115759/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum()
df.nunique()
df.dtypes
X = df[['Masked Date', 'col-A1', 'col-A2', 'col-A3', 'col-A4', 'col-B1', 'col-B2', 'col-B3', 'col-B4', 'col-C1', 'col-C2', 'col-C3', 'col-C4', 'col-D1', 'col-D2', 'col-D3', 'col-D4', 'col-E1', 'col-E2', 'col-E3', 'col-E4', 'col-F1', 'col-F2', 'col-F3', 'col-F4', 'col-G', 'col-H', 'col-I', 'col-J', 'col-L', 'col-M', 'col-N', 'col-N1', 'col-O', 'col-P', 'col-Q', 'col-R', 'col-S', 'col-T', 'col-U', 'col-V', 'col-W']]
y = df['col-Predict']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
predict = pd.read_csv('../input/al-majlis-ai-hackathon-investment-submission/submissionFile.csv')
predict
predict_data = predict.iloc[:, :1]
predict_data
for i in range(1, 42):
X = df['Masked Date']
y = df.iloc[:, i]
X = X.values.reshape(-1, 1)
lr_predict = LinearRegression()
lr_predict.fit(X, y)
predict_value = predict_data.iloc[:, :1]
predictions = lr_predict.predict(predict_value)
predict_data[i] = predictions
predict_data | code |
104115759/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, y_train)
y_predictions = lr.predict(X_test)
lr.score(X_test, y_test) | code |
104115759/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
predict = pd.read_csv('../input/al-majlis-ai-hackathon-investment-submission/submissionFile.csv')
predict
predict_data = predict.iloc[:, :1]
predict_data | code |
104115759/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df | code |
104115759/cell_39 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum()
df.nunique()
df.dtypes
X = df[['Masked Date', 'col-A1', 'col-A2', 'col-A3', 'col-A4', 'col-B1', 'col-B2', 'col-B3', 'col-B4', 'col-C1', 'col-C2', 'col-C3', 'col-C4', 'col-D1', 'col-D2', 'col-D3', 'col-D4', 'col-E1', 'col-E2', 'col-E3', 'col-E4', 'col-F1', 'col-F2', 'col-F3', 'col-F4', 'col-G', 'col-H', 'col-I', 'col-J', 'col-L', 'col-M', 'col-N', 'col-N1', 'col-O', 'col-P', 'col-Q', 'col-R', 'col-S', 'col-T', 'col-U', 'col-V', 'col-W']]
y = df['col-Predict']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
lr = LinearRegression()
lr.fit(X_train, y_train)
y_predictions = lr.predict(X_test)
lr.score(X_test, y_test)
predict = pd.read_csv('../input/al-majlis-ai-hackathon-investment-submission/submissionFile.csv')
predict
predict_data = predict.iloc[:, :1]
predict_data
for i in range(1, 42):
X = df['Masked Date']
y = df.iloc[:, i]
X = X.values.reshape(-1, 1)
lr_predict = LinearRegression()
lr_predict.fit(X, y)
predict_value = predict_data.iloc[:, :1]
predictions = lr_predict.predict(predict_value)
predict_data[i] = predictions
predict_data.columns = [['Masked Date', 'col-A1', 'col-A2', 'col-A3', 'col-A4', 'col-B1', 'col-B2', 'col-B3', 'col-B4', 'col-C1', 'col-C2', 'col-C3', 'col-C4', 'col-D1', 'col-D2', 'col-D3', 'col-D4', 'col-E1', 'col-E2', 'col-E3', 'col-E4', 'col-F1', 'col-F2', 'col-F3', 'col-F4', 'col-G', 'col-H', 'col-I', 'col-J', 'col-L', 'col-M', 'col-N', 'col-N1', 'col-O', 'col-P', 'col-Q', 'col-R', 'col-S', 'col-T', 'col-U', 'col-V', 'col-W']]
predictions = lr.predict(predict_data)
predictions | code |
104115759/cell_41 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
predict = pd.read_csv('../input/al-majlis-ai-hackathon-investment-submission/submissionFile.csv')
predict
predict_data = predict.iloc[:, :1]
predict_data
predict | code |
104115759/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.head() | code |
104115759/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns | code |
104115759/cell_32 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum()
df.nunique()
df.dtypes
X = df[['Masked Date', 'col-A1', 'col-A2', 'col-A3', 'col-A4', 'col-B1', 'col-B2', 'col-B3', 'col-B4', 'col-C1', 'col-C2', 'col-C3', 'col-C4', 'col-D1', 'col-D2', 'col-D3', 'col-D4', 'col-E1', 'col-E2', 'col-E3', 'col-E4', 'col-F1', 'col-F2', 'col-F3', 'col-F4', 'col-G', 'col-H', 'col-I', 'col-J', 'col-L', 'col-M', 'col-N', 'col-N1', 'col-O', 'col-P', 'col-Q', 'col-R', 'col-S', 'col-T', 'col-U', 'col-V', 'col-W']]
y = df['col-Predict']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
predict = pd.read_csv('../input/al-majlis-ai-hackathon-investment-submission/submissionFile.csv')
predict
predict_data = predict.iloc[:, :1]
predict_data
for i in range(1, 42):
X = df['Masked Date']
y = df.iloc[:, i]
X = X.values.reshape(-1, 1)
lr_predict = LinearRegression()
lr_predict.fit(X, y)
predict_value = predict_data.iloc[:, :1]
predictions = lr_predict.predict(predict_value)
predict_data[i] = predictions | code |
104115759/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
predict = pd.read_csv('../input/al-majlis-ai-hackathon-investment-submission/submissionFile.csv')
predict
len(predict) | code |
104115759/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns) | code |
104115759/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum()
df.nunique() | code |
104115759/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum()
df.nunique()
df.dtypes | code |
104115759/cell_38 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum()
df.nunique()
df.dtypes
X = df[['Masked Date', 'col-A1', 'col-A2', 'col-A3', 'col-A4', 'col-B1', 'col-B2', 'col-B3', 'col-B4', 'col-C1', 'col-C2', 'col-C3', 'col-C4', 'col-D1', 'col-D2', 'col-D3', 'col-D4', 'col-E1', 'col-E2', 'col-E3', 'col-E4', 'col-F1', 'col-F2', 'col-F3', 'col-F4', 'col-G', 'col-H', 'col-I', 'col-J', 'col-L', 'col-M', 'col-N', 'col-N1', 'col-O', 'col-P', 'col-Q', 'col-R', 'col-S', 'col-T', 'col-U', 'col-V', 'col-W']]
y = df['col-Predict']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
lr = LinearRegression()
lr.fit(X_train, y_train)
y_predictions = lr.predict(X_test)
lr.score(X_test, y_test)
predict = pd.read_csv('../input/al-majlis-ai-hackathon-investment-submission/submissionFile.csv')
predict
predict_data = predict.iloc[:, :1]
predict_data
for i in range(1, 42):
X = df['Masked Date']
y = df.iloc[:, i]
X = X.values.reshape(-1, 1)
lr_predict = LinearRegression()
lr_predict.fit(X, y)
predict_value = predict_data.iloc[:, :1]
predictions = lr_predict.predict(predict_value)
predict_data[i] = predictions
predict_data.columns = [['Masked Date', 'col-A1', 'col-A2', 'col-A3', 'col-A4', 'col-B1', 'col-B2', 'col-B3', 'col-B4', 'col-C1', 'col-C2', 'col-C3', 'col-C4', 'col-D1', 'col-D2', 'col-D3', 'col-D4', 'col-E1', 'col-E2', 'col-E3', 'col-E4', 'col-F1', 'col-F2', 'col-F3', 'col-F4', 'col-G', 'col-H', 'col-I', 'col-J', 'col-L', 'col-M', 'col-N', 'col-N1', 'col-O', 'col-P', 'col-Q', 'col-R', 'col-S', 'col-T', 'col-U', 'col-V', 'col-W']]
predictions = lr.predict(predict_data) | code |
104115759/cell_43 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum()
df.nunique()
df.dtypes
X = df[['Masked Date', 'col-A1', 'col-A2', 'col-A3', 'col-A4', 'col-B1', 'col-B2', 'col-B3', 'col-B4', 'col-C1', 'col-C2', 'col-C3', 'col-C4', 'col-D1', 'col-D2', 'col-D3', 'col-D4', 'col-E1', 'col-E2', 'col-E3', 'col-E4', 'col-F1', 'col-F2', 'col-F3', 'col-F4', 'col-G', 'col-H', 'col-I', 'col-J', 'col-L', 'col-M', 'col-N', 'col-N1', 'col-O', 'col-P', 'col-Q', 'col-R', 'col-S', 'col-T', 'col-U', 'col-V', 'col-W']]
y = df['col-Predict']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
predict = pd.read_csv('../input/al-majlis-ai-hackathon-investment-submission/submissionFile.csv')
predict
predict_data = predict.iloc[:, :1]
predict_data
for i in range(1, 42):
X = df['Masked Date']
y = df.iloc[:, i]
X = X.values.reshape(-1, 1)
lr_predict = LinearRegression()
lr_predict.fit(X, y)
predict_value = predict_data.iloc[:, :1]
predictions = lr_predict.predict(predict_value)
predict_data[i] = predictions
df['col-Predict'] | code |
104115759/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.tail() | code |
104115759/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
predict = pd.read_csv('../input/al-majlis-ai-hackathon-investment-submission/submissionFile.csv')
predict | code |
104115759/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T | code |
106198491/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0])
df['Total'] = df.iloc[:, 4:10].sum(axis=1)
df
cols = list(df.columns)
df = df[cols[:4] + [cols[-1]] + cols[4:12]]
df
df.to_csv('modified_pokemon_data.csv')
df = pd.read_csv('./modified_pokemon_data.csv')
df
df.loc[df['Type 1'] == 'Flamer', 'Legendary'] = True
df = pd.read_csv('./modified_pokemon_data.csv')
df
df.loc[df['Total'] > 500, ['Generation', 'Legendary']] = 'Highest'
df.loc[df['Total'] > 500, ['Generation', 'Legendary']] = ['Highest Gen', 'Highest Legend']
df = pd.read_csv('./modified_pokemon_data.csv')
df | code |
106198491/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1] | code |
106198491/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0])
df['Total'] = df.iloc[:, 4:10].sum(axis=1)
df
cols = list(df.columns)
df = df[cols[:4] + [cols[-1]] + cols[4:12]]
df
df.to_csv('modified_pokemon_data.csv')
df = pd.read_csv('./modified_pokemon_data.csv')
df
df.loc[df['Type 1'] == 'Flamer', 'Legendary'] = True
df = pd.read_csv('./modified_pokemon_data.csv')
df | code |
106198491/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.describe()
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0]) | code |
106198491/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0])
df['Total'] = df.iloc[:, 4:10].sum(axis=1)
df
cols = list(df.columns)
df = df[cols[:4] + [cols[-1]] + cols[4:12]]
df | code |
106198491/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0])
df['Total'] = df.iloc[:, 4:10].sum(axis=1)
df
cols = list(df.columns)
df = df[cols[:4] + [cols[-1]] + cols[4:12]]
df
df.to_csv('modified_pokemon_data.csv')
df = pd.read_csv('./modified_pokemon_data.csv')
df
df.loc[df['Type 1'] == 'Flamer', 'Legendary'] = True
df = pd.read_csv('./modified_pokemon_data.csv')
df
df.loc[df['Total'] > 500, ['Generation', 'Legendary']] = 'Highest'
df.loc[df['Total'] > 500, ['Generation', 'Legendary']] = ['Highest Gen', 'Highest Legend']
df = pd.read_csv('./modified_pokemon_data.csv')
df
df.groupby(['Type 1']).mean().sort_values('Defense', ascending=False)['Defense']
df.groupby(['Type 1']).mean().sort_values('Attack', ascending=False)['Attack']
df.groupby(['Type 1']).mean().sort_values('HP', ascending=False)['HP'] | code |
106198491/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0])
df['Total'] = df.iloc[:, 4:10].sum(axis=1)
df
cols = list(df.columns)
df = df[cols[:4] + [cols[-1]] + cols[4:12]]
df
df.to_csv('modified_pokemon_data.csv')
df = pd.read_csv('./modified_pokemon_data.csv')
df
df.loc[df['Type 1'] == 'Flamer', 'Legendary'] = True
df = pd.read_csv('./modified_pokemon_data.csv')
df
df.loc[df['Total'] > 500, ['Generation', 'Legendary']] = 'Highest'
df.loc[df['Total'] > 500, ['Generation', 'Legendary']] = ['Highest Gen', 'Highest Legend']
df = pd.read_csv('./modified_pokemon_data.csv')
df
df.groupby(['Type 1']).mean().sort_values('Defense', ascending=False)['Defense']
df.groupby(['Type 1']).mean().sort_values('Attack', ascending=False)['Attack']
df.groupby(['Type 1']).mean().sort_values('HP', ascending=False)['HP']
df.groupby(['Type 1']).count()['Name'] | code |
106198491/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0])
df['Total'] = df.iloc[:, 4:10].sum(axis=1)
df
cols = list(df.columns)
df = df[cols[:4] + [cols[-1]] + cols[4:12]]
df
df.to_csv('modified_pokemon_data.csv')
df = pd.read_csv('./modified_pokemon_data.csv')
df
df.loc[df['Type 1'] == 'Flamer', 'Legendary'] = True
df = pd.read_csv('./modified_pokemon_data.csv')
df
df.loc[df['Total'] > 500, ['Generation', 'Legendary']] = 'Highest'
df.loc[df['Total'] > 500, ['Generation', 'Legendary']] = ['Highest Gen', 'Highest Legend']
df = pd.read_csv('./modified_pokemon_data.csv')
df
df.groupby(['Type 1']).mean().sort_values('Defense', ascending=False)['Defense']
df.groupby(['Type 1']).mean().sort_values('Attack', ascending=False)['Attack']
df.groupby(['Type 1']).mean().sort_values('HP', ascending=False)['HP']
df.groupby(['Type 1']).count()['Name']
df.groupby(['Type 1', 'Type 2']).count()['Name'] | code |
106198491/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0])
df['Total'] = df.iloc[:, 4:10].sum(axis=1)
df
cols = list(df.columns)
df = df[cols[:4] + [cols[-1]] + cols[4:12]]
df
df.to_csv('modified_pokemon_data.csv')
df = pd.read_csv('./modified_pokemon_data.csv')
df | code |
106198491/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.head() | code |
105210749/cell_21 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cylindernumber.apply(w2n.word_to_num)
(df.doornumber, df.cylindernumber)
df = df.drop('car_ID', axis=1)
df.columns
df.rename(columns={'fueltype': 'fuelType', 'doornumber': 'num_Doors', 'carbody': 'carBody', 'drivewheel': 'driveWheel', 'enginelocation': 'engineLocation', 'wheelbase': 'wheelBase', 'carlength': 'carLength', 'carwidth': 'carWidth', 'carheight': 'carHeight', 'curbweight': 'curbWeight', 'enginetype': 'engineType', 'cylindernumber': 'num_Cyl', 'enginesize': 'engineSize', 'fuelsystem': 'fuelSystem', 'boreratio': 'boreRatio', 'compressionratio': 'compressionRatio', 'horsepower': 'horsePower', 'peakrpm': 'peakRPM', 'citympg': 'cityMPG', 'highwaympg': 'highwayMPG'}, inplace=True)
df.select_dtypes('object')
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['fuelType'] = le.fit_transform(df['fuelType'])
df['aspiration'] = le.fit_transform(df['aspiration'])
df['carBody'] = le.fit_transform(df['carBody'])
df['driveWheel'] = le.fit_transform(df['driveWheel'])
df['engineLocation'] = le.fit_transform(df['engineLocation'])
df['engineType'] = le.fit_transform(df['engineType'])
df['fuelSystem'] = le.fit_transform(df['fuelSystem'])
df | code |
105210749/cell_25 | [
"text_html_output_1.png"
] | from word2number import w2n
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cylindernumber.apply(w2n.word_to_num)
(df.doornumber, df.cylindernumber)
df = df.drop('car_ID', axis=1)
df.columns
df.rename(columns={'fueltype': 'fuelType', 'doornumber': 'num_Doors', 'carbody': 'carBody', 'drivewheel': 'driveWheel', 'enginelocation': 'engineLocation', 'wheelbase': 'wheelBase', 'carlength': 'carLength', 'carwidth': 'carWidth', 'carheight': 'carHeight', 'curbweight': 'curbWeight', 'enginetype': 'engineType', 'cylindernumber': 'num_Cyl', 'enginesize': 'engineSize', 'fuelsystem': 'fuelSystem', 'boreratio': 'boreRatio', 'compressionratio': 'compressionRatio', 'horsepower': 'horsePower', 'peakrpm': 'peakRPM', 'citympg': 'cityMPG', 'highwaympg': 'highwayMPG'}, inplace=True)
df.select_dtypes('object')
df.plot(kind='box', subplots=True, layout=(5, 5), figsize=(20, 20))
plt.show() | code |
105210749/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.info() | code |
105210749/cell_23 | [
"text_plain_output_1.png"
] | from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cylindernumber.apply(w2n.word_to_num)
(df.doornumber, df.cylindernumber)
df = df.drop('car_ID', axis=1)
df.columns
df.rename(columns={'fueltype': 'fuelType', 'doornumber': 'num_Doors', 'carbody': 'carBody', 'drivewheel': 'driveWheel', 'enginelocation': 'engineLocation', 'wheelbase': 'wheelBase', 'carlength': 'carLength', 'carwidth': 'carWidth', 'carheight': 'carHeight', 'curbweight': 'curbWeight', 'enginetype': 'engineType', 'cylindernumber': 'num_Cyl', 'enginesize': 'engineSize', 'fuelsystem': 'fuelSystem', 'boreratio': 'boreRatio', 'compressionratio': 'compressionRatio', 'horsepower': 'horsePower', 'peakrpm': 'peakRPM', 'citympg': 'cityMPG', 'highwaympg': 'highwayMPG'}, inplace=True)
df.select_dtypes('object')
[df['fuelType'].value_counts(), df['aspiration'].value_counts(), df['carBody'].value_counts(), df['driveWheel'].value_counts(), df['engineLocation'].value_counts(), df['engineType'].value_counts(), df['fuelSystem'].value_counts()] | code |
105210749/cell_30 | [
"text_plain_output_1.png"
] | from word2number import w2n
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cylindernumber.apply(w2n.word_to_num)
(df.doornumber, df.cylindernumber)
df = df.drop('car_ID', axis=1)
df.columns
df.rename(columns={'fueltype': 'fuelType', 'doornumber': 'num_Doors', 'carbody': 'carBody', 'drivewheel': 'driveWheel', 'enginelocation': 'engineLocation', 'wheelbase': 'wheelBase', 'carlength': 'carLength', 'carwidth': 'carWidth', 'carheight': 'carHeight', 'curbweight': 'curbWeight', 'enginetype': 'engineType', 'cylindernumber': 'num_Cyl', 'enginesize': 'engineSize', 'fuelsystem': 'fuelSystem', 'boreratio': 'boreRatio', 'compressionratio': 'compressionRatio', 'horsepower': 'horsePower', 'peakrpm': 'peakRPM', 'citympg': 'cityMPG', 'highwaympg': 'highwayMPG'}, inplace=True)
df.select_dtypes('object')
df.columns | code |
105210749/cell_20 | [
"text_plain_output_1.png"
] | from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cylindernumber.apply(w2n.word_to_num)
(df.doornumber, df.cylindernumber)
df = df.drop('car_ID', axis=1)
df.columns
df.rename(columns={'fueltype': 'fuelType', 'doornumber': 'num_Doors', 'carbody': 'carBody', 'drivewheel': 'driveWheel', 'enginelocation': 'engineLocation', 'wheelbase': 'wheelBase', 'carlength': 'carLength', 'carwidth': 'carWidth', 'carheight': 'carHeight', 'curbweight': 'curbWeight', 'enginetype': 'engineType', 'cylindernumber': 'num_Cyl', 'enginesize': 'engineSize', 'fuelsystem': 'fuelSystem', 'boreratio': 'boreRatio', 'compressionratio': 'compressionRatio', 'horsepower': 'horsePower', 'peakrpm': 'peakRPM', 'citympg': 'cityMPG', 'highwaympg': 'highwayMPG'}, inplace=True)
df.select_dtypes('object')
[df['fuelType'].value_counts(), df['aspiration'].value_counts(), df['carBody'].value_counts(), df['driveWheel'].value_counts(), df['engineLocation'].value_counts(), df['engineType'].value_counts(), df['fuelSystem'].value_counts()] | code |
105210749/cell_40 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from word2number import w2n
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cylindernumber.apply(w2n.word_to_num)
(df.doornumber, df.cylindernumber)
df = df.drop('car_ID', axis=1)
df.columns
df.rename(columns={'fueltype': 'fuelType', 'doornumber': 'num_Doors', 'carbody': 'carBody', 'drivewheel': 'driveWheel', 'enginelocation': 'engineLocation', 'wheelbase': 'wheelBase', 'carlength': 'carLength', 'carwidth': 'carWidth', 'carheight': 'carHeight', 'curbweight': 'curbWeight', 'enginetype': 'engineType', 'cylindernumber': 'num_Cyl', 'enginesize': 'engineSize', 'fuelsystem': 'fuelSystem', 'boreratio': 'boreRatio', 'compressionratio': 'compressionRatio', 'horsepower': 'horsePower', 'peakrpm': 'peakRPM', 'citympg': 'cityMPG', 'highwaympg': 'highwayMPG'}, inplace=True)
df.select_dtypes('object')
df.columns
cars = df[['symboling', 'fuelType', 'aspiration', 'num_Doors', 'carBody', 'driveWheel', 'engineLocation', 'wheelBase', 'carLength', 'carWidth', 'carHeight', 'curbWeight', 'engineType', 'num_Cyl', 'engineSize', 'fuelSystem', 'boreRatio', 'stroke', 'compressionRatio', 'horsePower', 'peakRPM', 'cityMPG', 'highwayMPG', 'price']]
x = cars.drop(['price'], axis=1).values
y = cars['price'].values
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(x_train, y_train)
(reg.score(x_train, y_train), reg.score(x_test, y_test))
pd.DataFrame(reg.coef_, cars.columns[:-1], columns=['Coeficient'])
y_pred = reg.predict(x_test)
df1 = pd.DataFrame({'Y_test': y_test, 'Y_pred': y_pred})
df1.head(10) | code |
105210749/cell_39 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from word2number import w2n
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cylindernumber.apply(w2n.word_to_num)
(df.doornumber, df.cylindernumber)
df = df.drop('car_ID', axis=1)
df.columns
df.rename(columns={'fueltype': 'fuelType', 'doornumber': 'num_Doors', 'carbody': 'carBody', 'drivewheel': 'driveWheel', 'enginelocation': 'engineLocation', 'wheelbase': 'wheelBase', 'carlength': 'carLength', 'carwidth': 'carWidth', 'carheight': 'carHeight', 'curbweight': 'curbWeight', 'enginetype': 'engineType', 'cylindernumber': 'num_Cyl', 'enginesize': 'engineSize', 'fuelsystem': 'fuelSystem', 'boreratio': 'boreRatio', 'compressionratio': 'compressionRatio', 'horsepower': 'horsePower', 'peakrpm': 'peakRPM', 'citympg': 'cityMPG', 'highwaympg': 'highwayMPG'}, inplace=True)
df.select_dtypes('object')
df.columns
cars = df[['symboling', 'fuelType', 'aspiration', 'num_Doors', 'carBody', 'driveWheel', 'engineLocation', 'wheelBase', 'carLength', 'carWidth', 'carHeight', 'curbWeight', 'engineType', 'num_Cyl', 'engineSize', 'fuelSystem', 'boreRatio', 'stroke', 'compressionRatio', 'horsePower', 'peakRPM', 'cityMPG', 'highwayMPG', 'price']]
x = cars.drop(['price'], axis=1).values
y = cars['price'].values
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(x_train, y_train)
(reg.score(x_train, y_train), reg.score(x_test, y_test))
pd.DataFrame(reg.coef_, cars.columns[:-1], columns=['Coeficient']) | code |
105210749/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum() | code |
105210749/cell_18 | [
"text_plain_output_1.png"
] | from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cylindernumber.apply(w2n.word_to_num)
(df.doornumber, df.cylindernumber)
df = df.drop('car_ID', axis=1)
df.columns
df.rename(columns={'fueltype': 'fuelType', 'doornumber': 'num_Doors', 'carbody': 'carBody', 'drivewheel': 'driveWheel', 'enginelocation': 'engineLocation', 'wheelbase': 'wheelBase', 'carlength': 'carLength', 'carwidth': 'carWidth', 'carheight': 'carHeight', 'curbweight': 'curbWeight', 'enginetype': 'engineType', 'cylindernumber': 'num_Cyl', 'enginesize': 'engineSize', 'fuelsystem': 'fuelSystem', 'boreratio': 'boreRatio', 'compressionratio': 'compressionRatio', 'horsepower': 'horsePower', 'peakrpm': 'peakRPM', 'citympg': 'cityMPG', 'highwaympg': 'highwayMPG'}, inplace=True)
df.select_dtypes('object') | code |
105210749/cell_28 | [
"text_plain_output_1.png"
] | from word2number import w2n
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cylindernumber.apply(w2n.word_to_num)
(df.doornumber, df.cylindernumber)
df = df.drop('car_ID', axis=1)
df.columns
df.rename(columns={'fueltype': 'fuelType', 'doornumber': 'num_Doors', 'carbody': 'carBody', 'drivewheel': 'driveWheel', 'enginelocation': 'engineLocation', 'wheelbase': 'wheelBase', 'carlength': 'carLength', 'carwidth': 'carWidth', 'carheight': 'carHeight', 'curbweight': 'curbWeight', 'enginetype': 'engineType', 'cylindernumber': 'num_Cyl', 'enginesize': 'engineSize', 'fuelsystem': 'fuelSystem', 'boreratio': 'boreRatio', 'compressionratio': 'compressionRatio', 'horsepower': 'horsePower', 'peakrpm': 'peakRPM', 'citympg': 'cityMPG', 'highwaympg': 'highwayMPG'}, inplace=True)
df.select_dtypes('object')
plt.figure(figsize=(20, 10))
sns.heatmap(df.corr(), annot=True, cmap='mako')
plt.show() | code |
105210749/cell_16 | [
"text_plain_output_1.png"
] | from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cylindernumber.apply(w2n.word_to_num)
(df.doornumber, df.cylindernumber)
df = df.drop('car_ID', axis=1)
df.columns
df.rename(columns={'fueltype': 'fuelType', 'doornumber': 'num_Doors', 'carbody': 'carBody', 'drivewheel': 'driveWheel', 'enginelocation': 'engineLocation', 'wheelbase': 'wheelBase', 'carlength': 'carLength', 'carwidth': 'carWidth', 'carheight': 'carHeight', 'curbweight': 'curbWeight', 'enginetype': 'engineType', 'cylindernumber': 'num_Cyl', 'enginesize': 'engineSize', 'fuelsystem': 'fuelSystem', 'boreratio': 'boreRatio', 'compressionratio': 'compressionRatio', 'horsepower': 'horsePower', 'peakrpm': 'peakRPM', 'citympg': 'cityMPG', 'highwaympg': 'highwayMPG'}, inplace=True)
df.head() | code |
105210749/cell_38 | [
"image_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(x_train, y_train)
(reg.score(x_train, y_train), reg.score(x_test, y_test)) | code |
105210749/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.head() | code |
105210749/cell_17 | [
"text_plain_output_1.png"
] | from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cylindernumber.apply(w2n.word_to_num)
(df.doornumber, df.cylindernumber)
df = df.drop('car_ID', axis=1)
df.columns
df.rename(columns={'fueltype': 'fuelType', 'doornumber': 'num_Doors', 'carbody': 'carBody', 'drivewheel': 'driveWheel', 'enginelocation': 'engineLocation', 'wheelbase': 'wheelBase', 'carlength': 'carLength', 'carwidth': 'carWidth', 'carheight': 'carHeight', 'curbweight': 'curbWeight', 'enginetype': 'engineType', 'cylindernumber': 'num_Cyl', 'enginesize': 'engineSize', 'fuelsystem': 'fuelSystem', 'boreratio': 'boreRatio', 'compressionratio': 'compressionRatio', 'horsepower': 'horsePower', 'peakrpm': 'peakRPM', 'citympg': 'cityMPG', 'highwaympg': 'highwayMPG'}, inplace=True)
df.info() | code |
105210749/cell_14 | [
"text_html_output_1.png"
] | from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cylindernumber.apply(w2n.word_to_num)
(df.doornumber, df.cylindernumber)
df = df.drop('car_ID', axis=1)
df.columns | code |
105210749/cell_10 | [
"text_html_output_1.png"
] | pip install word2number | code |
105210749/cell_37 | [
"image_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(x_train, y_train) | code |
105210749/cell_12 | [
"text_plain_output_1.png"
] | from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cylindernumber.apply(w2n.word_to_num)
(df.doornumber, df.cylindernumber) | code |
105210749/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.describe() | code |
2014525/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
df = df[df['calories'] < 10000]
df.dropna(inplace=True)
print('Is this variable numeric?')
df['rating'].dtype == 'float' | code |
2014525/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/epi_r.csv')
df = df[df['calories'] < 10000]
df.dropna(inplace=True)
sns.regplot(df['calories'], df['dessert'], fit_reg=False) | code |
2014525/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
df.head() | code |
2014525/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/epi_r.csv')
df = df[df['calories'] < 10000]
df.dropna(inplace=True)
sns.regplot(df['calories'], df['dessert']) | code |
2014525/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
df = df[df['calories'] < 10000]
df.dropna(inplace=True)
print('Is this variable only integers?')
df['rating'].dtype == 'int' | code |
129016727/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset['having_At_Symbol'].value_counts() | code |
129016727/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset['Google_Index'].value_counts() | code |
129016727/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset.head() | code |
129016727/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
LR = LogisticRegression()
LR.fit(X_train, y_train)
y_pred = LR.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.tree import DecisionTreeClassifier
DT = DecisionTreeClassifier()
DT.fit(X_train, y_train)
y_pred = DT.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.svm import SVC
SVM = SVC()
SVM.fit(X_train, y_train)
y_pred = SVM.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.ensemble import RandomForestClassifier
RF = RandomForestClassifier()
RF.fit(X_train, y_train)
y_pred = RF.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.naive_bayes import GaussianNB
NB = GaussianNB()
NB.fit(X_train, y_train)
y_pred = NB.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.neighbors import KNeighborsClassifier
k = 5
KNN = KNeighborsClassifier(n_neighbors=k)
KNN.fit(X_train, y_train)
y_pred = KNN.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy) | code |
129016727/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
selected_features = ['URLURL_Length', 'having_At_Symbol', 'double_slash_redirecting', 'Prefix_Suffix', 'Page_Rank', 'Google_Index', 'Result']
df = dataset[selected_features]
df = df.drop_duplicates()
df.shape
df['Result'].value_counts(normalize=True) | code |
129016727/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
LR = LogisticRegression()
LR.fit(X_train, y_train)
y_pred = LR.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.tree import DecisionTreeClassifier
DT = DecisionTreeClassifier()
DT.fit(X_train, y_train)
y_pred = DT.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy) | code |
129016727/cell_33 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
LR = LogisticRegression()
LR.fit(X_train, y_train)
y_pred = LR.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.tree import DecisionTreeClassifier
DT = DecisionTreeClassifier()
DT.fit(X_train, y_train)
y_pred = DT.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.svm import SVC
SVM = SVC()
SVM.fit(X_train, y_train)
y_pred = SVM.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.ensemble import RandomForestClassifier
RF = RandomForestClassifier()
RF.fit(X_train, y_train)
y_pred = RF.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.naive_bayes import GaussianNB
NB = GaussianNB()
NB.fit(X_train, y_train)
y_pred = NB.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy) | code |
129016727/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
selected_features = ['URLURL_Length', 'having_At_Symbol', 'double_slash_redirecting', 'Prefix_Suffix', 'Page_Rank', 'Google_Index', 'Result']
df = dataset[selected_features]
df = df.drop_duplicates()
df.shape | code |
129016727/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset['Result'].value_counts() | code |
129016727/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
LR = LogisticRegression()
LR.fit(X_train, y_train)
y_pred = LR.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc
y_pred_prob = LR.predict_proba(X_test)[:, 1]
fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)
auc_score = auc(fpr, tpr)
plt.plot(fpr, tpr, label='ROC curve (AUC = %0.2f)' % auc_score)
plt.plot([0, 1], [0, 1], 'k--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.legend(loc='lower right')
plt.show() | code |
129016727/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
selected_features = ['URLURL_Length', 'having_At_Symbol', 'double_slash_redirecting', 'Prefix_Suffix', 'Page_Rank', 'Google_Index', 'Result']
df = dataset[selected_features]
df = df.drop_duplicates()
df.shape
X = df.drop('Result', axis=1)
y = df['Result']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0, stratify=y) | code |
129016727/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset['URLURL_Length'].value_counts() | code |
129016727/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
selected_features = ['URLURL_Length', 'having_At_Symbol', 'double_slash_redirecting', 'Prefix_Suffix', 'Page_Rank', 'Google_Index', 'Result']
df = dataset[selected_features]
df.head() | code |
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