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17118879/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pathlib
import random
import seaborn as sns
import tensorflow as tf
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sample_sub_df = pd.read_csv('../input/train.csv')
train_images_path = '../input/train_images'
test_images_path = '../input/test_images'
train_df[train_df.id_code == '5d024177e214']
classes_dist = pd.DataFrame(train_df['diagnosis'].value_counts()/train_df.shape[0]).reset_index()
# barplot
ax = sns.barplot(x="index", y="diagnosis", data=classes_dist)
# Imbalanced dataset with 49% - no DR, 8% proliferative - i.e most severe DR
# Model Building - Need to do oversampling for minority classes
root_path = pathlib.Path(train_images_path)
for item in root_path.iterdir():
break
all_paths = list(root_path.glob('*.png'))
all_paths[0]
all_paths = [str(path) for path in all_paths]
random.shuffle(all_paths)
img = tf.read_file(all_paths)
img
def preprocess_image(image):
img_tensor = tf.image.decode_png(image, channels=3)
img_tensor = tf.cast(img_tensor, tf.float32)
img_tensor /= 255.0
return img_tensor
def load_and_preprocess_image(path):
image = tf.read_file(path)
return preprocess_image(image)
train_df.columns
train_df['image_path'] = '../input/train_images/' + train_df['id_code']
np.array(train_df['diagnosis'])
labels = tf.convert_to_tensor(np.array(train_df['diagnosis']), dtype=tf.int32)
paths = tf.convert_to_tensor(np.array(train_df['image_path']), dtype=tf.string)
image, label = tf.train.slice_input_producer([paths, labels], shuffle=True)
path_ds = tf.data.Dataset.from_tensor_slices(train_df['image_path'])
AUTOTUNE = tf.data.experimental.AUTOTUNE
image_ds = path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)
image_ds.take(1) | code |
17118879/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sample_sub_df = pd.read_csv('../input/train.csv')
train_df.head(4) | code |
17118879/cell_15 | [
"text_plain_output_1.png"
] | import pathlib
train_images_path = '../input/train_images'
test_images_path = '../input/test_images'
root_path = pathlib.Path(train_images_path)
for item in root_path.iterdir():
break
all_paths = list(root_path.glob('*.png'))
all_paths[0] | code |
17118879/cell_3 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
os.getcwd()
os.listdir() | code |
17118879/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sample_sub_df = pd.read_csv('../input/train.csv')
train_df[train_df.id_code == '5d024177e214']
classes_dist = pd.DataFrame(train_df['diagnosis'].value_counts()/train_df.shape[0]).reset_index()
# barplot
ax = sns.barplot(x="index", y="diagnosis", data=classes_dist)
# Imbalanced dataset with 49% - no DR, 8% proliferative - i.e most severe DR
# Model Building - Need to do oversampling for minority classes
train_df.columns
train_df['image_path'] = '../input/train_images/' + train_df['id_code']
np.array(train_df['diagnosis']) | code |
17118879/cell_14 | [
"text_html_output_1.png"
] | import pathlib
train_images_path = '../input/train_images'
test_images_path = '../input/test_images'
root_path = pathlib.Path(train_images_path)
for item in root_path.iterdir():
print(item)
break | code |
17118879/cell_22 | [
"text_plain_output_1.png"
] | import pathlib
import random
import tensorflow as tf
train_images_path = '../input/train_images'
test_images_path = '../input/test_images'
root_path = pathlib.Path(train_images_path)
for item in root_path.iterdir():
break
all_paths = list(root_path.glob('*.png'))
all_paths[0]
all_paths = [str(path) for path in all_paths]
random.shuffle(all_paths)
img = tf.read_file(all_paths)
img
def preprocess_image(image):
img_tensor = tf.image.decode_png(image, channels=3)
img_tensor = tf.cast(img_tensor, tf.float32)
img_tensor /= 255.0
return img_tensor
def load_and_preprocess_image(path):
image = tf.read_file(path)
return preprocess_image(image)
print(img_tensor) | code |
17118879/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sample_sub_df = pd.read_csv('../input/train.csv')
train_df[train_df.id_code == '5d024177e214'] | code |
17118879/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sample_sub_df = pd.read_csv('../input/train.csv')
test_df.info() | code |
128029150/cell_21 | [
"text_plain_output_1.png"
] | from pandas import DataFrame
from sklearn import neighbors
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics import classification_report
from sklearn.metrics import mean_squared_error
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_meta[['asin', 'overall', 'unixReviewTime']]
data_meta_1
data_meta_2 = data_meta[['asin', 'overall', 'summaryRev']]
data_meta_2
regEx = re.compile('[^a-z]+')
def cleanReviews(reviewText):
reviewText = reviewText.lower()
reviewText = regEx.sub(' ', reviewText).strip()
return reviewText
data_meta_2['summaryClean'] = data_meta_2['summaryRev'].apply(cleanReviews)
data_meta_2 = data_meta_2.reset_index()
reviews = data_meta_2['summaryClean']
countVector = CountVectorizer(max_features=300, stop_words='english')
transformedReviews = countVector.fit_transform(reviews)
dfReviews = DataFrame(transformedReviews.A, columns=countVector.get_feature_names())
dfReviews = dfReviews.astype(int)
X = np.array(dfReviews)
tpercent = 0.9
tsize = int(np.floor(tpercent * len(dfReviews)))
dfReviews_train = X[:tsize]
dfReviews_test = X[tsize:]
lentrain = len(dfReviews_train)
lentest = len(dfReviews_test)
df5_train_target = data_meta_2['overall'][:lentrain]
df5_test_target = data_meta_2['overall'][lentrain:lentrain + lentest]
df5_train_target = df5_train_target.astype(int)
df5_test_target = df5_test_target.astype(int)
n_neighbors = 3
knnclf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
knnclf.fit(dfReviews_train, df5_train_target)
knnpreds_test = knnclf.predict(dfReviews_test)
df5_train_target = data_meta_2['overall'][:lentrain]
df5_test_target = data_meta_2['overall'][lentrain:lentrain + lentest]
df5_train_target = df5_train_target.astype(int)
df5_test_target = df5_test_target.astype(int)
n_neighbors = 28
knnclf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
knnclf.fit(dfReviews_train, df5_train_target)
knnpreds_test = knnclf.predict(dfReviews_test)
print(mean_squared_error(df5_test_target, knnpreds_test)) | code |
128029150/cell_13 | [
"text_html_output_1.png"
] | from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_meta[['asin', 'overall', 'unixReviewTime']]
data_meta_1
data_meta_2 = data_meta[['asin', 'overall', 'summaryRev']]
data_meta_2
regEx = re.compile('[^a-z]+')
def cleanReviews(reviewText):
reviewText = reviewText.lower()
reviewText = regEx.sub(' ', reviewText).strip()
return reviewText
data_meta_2['summaryClean'] = data_meta_2['summaryRev'].apply(cleanReviews)
data_meta_2 = data_meta_2.reset_index()
reviews = data_meta_2['summaryClean']
countVector = CountVectorizer(max_features=300, stop_words='english')
transformedReviews = countVector.fit_transform(reviews)
dfReviews = DataFrame(transformedReviews.A, columns=countVector.get_feature_names())
dfReviews = dfReviews.astype(int)
X = np.array(dfReviews)
tpercent = 0.9
tsize = int(np.floor(tpercent * len(dfReviews)))
dfReviews_train = X[:tsize]
dfReviews_test = X[tsize:]
lentrain = len(dfReviews_train)
lentest = len(dfReviews_test)
print(lentrain)
print(lentest) | code |
128029150/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_meta[['asin', 'overall', 'unixReviewTime']]
data_meta_1
data_meta_2 = data_meta[['asin', 'overall', 'summaryRev']]
data_meta_2
regEx = re.compile('[^a-z]+')
def cleanReviews(reviewText):
reviewText = reviewText.lower()
reviewText = regEx.sub(' ', reviewText).strip()
return reviewText
data_meta_2['summaryClean'] = data_meta_2['summaryRev'].apply(cleanReviews)
data_meta_2 = data_meta_2.reset_index()
data_meta_2 | code |
128029150/cell_25 | [
"text_plain_output_1.png"
] | from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.neighbors import NearestNeighbors
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_meta[['asin', 'overall', 'unixReviewTime']]
data_meta_1
data_meta_2 = data_meta[['asin', 'overall', 'summaryRev']]
data_meta_2
regEx = re.compile('[^a-z]+')
def cleanReviews(reviewText):
reviewText = reviewText.lower()
reviewText = regEx.sub(' ', reviewText).strip()
return reviewText
data_meta_2['summaryClean'] = data_meta_2['summaryRev'].apply(cleanReviews)
data_meta_2 = data_meta_2.reset_index()
reviews = data_meta_2['summaryClean']
countVector = CountVectorizer(max_features=300, stop_words='english')
transformedReviews = countVector.fit_transform(reviews)
dfReviews = DataFrame(transformedReviews.A, columns=countVector.get_feature_names())
dfReviews = dfReviews.astype(int)
X = np.array(dfReviews)
tpercent = 0.9
tsize = int(np.floor(tpercent * len(dfReviews)))
dfReviews_train = X[:tsize]
dfReviews_test = X[tsize:]
lentrain = len(dfReviews_train)
lentest = len(dfReviews_test)
neighbor = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(dfReviews_train)
distances, indices = neighbor.kneighbors(dfReviews_train)
for i in range(lentest):
a = neighbor.kneighbors([dfReviews_test[i]])
related_product_list = a[1]
first_related_product = [item[0] for item in related_product_list]
first_related_product = str(first_related_product).strip('[]')
first_related_product = int(first_related_product)
second_related_product = [item[1] for item in related_product_list]
second_related_product = str(second_related_product).strip('[]')
second_related_product = int(second_related_product)
X = np.array(dfReviews)
tpercent = 0.85
tsize = int(np.floor(tpercent * len(dfReviews)))
dfReviews_train = X[:tsize]
dfReviews_test = X[tsize:]
lentrain = len(dfReviews_train)
lentest = len(dfReviews_test)
neighbor = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(dfReviews_train)
distances, indices = neighbor.kneighbors(dfReviews_train)
for i in range(lentest):
a = neighbor.kneighbors([dfReviews_test[i]])
related_product_list = a[1]
first_related_product = [item[0] for item in related_product_list]
first_related_product = str(first_related_product).strip('[]')
first_related_product = int(first_related_product)
second_related_product = [item[1] for item in related_product_list]
second_related_product = str(second_related_product).strip('[]')
second_related_product = int(second_related_product)
print('Based on product reviews, for ', data_meta_2['asin'][lentrain + i], ' average rating is ', data_meta_2['overall'][lentrain + i])
print('The first similar product is ', data_meta_2['asin'][first_related_product], ' average rating is ', data_meta_2['overall'][first_related_product])
print('The second similar product is ', data_meta_2['asin'][second_related_product], ' average rating is ', data_meta_2['overall'][second_related_product])
print('-----------------------------------------------------------') | code |
128029150/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'") | code |
128029150/cell_20 | [
"text_plain_output_1.png"
] | from pandas import DataFrame
from sklearn import neighbors
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_meta[['asin', 'overall', 'unixReviewTime']]
data_meta_1
data_meta_2 = data_meta[['asin', 'overall', 'summaryRev']]
data_meta_2
regEx = re.compile('[^a-z]+')
def cleanReviews(reviewText):
reviewText = reviewText.lower()
reviewText = regEx.sub(' ', reviewText).strip()
return reviewText
data_meta_2['summaryClean'] = data_meta_2['summaryRev'].apply(cleanReviews)
data_meta_2 = data_meta_2.reset_index()
reviews = data_meta_2['summaryClean']
countVector = CountVectorizer(max_features=300, stop_words='english')
transformedReviews = countVector.fit_transform(reviews)
dfReviews = DataFrame(transformedReviews.A, columns=countVector.get_feature_names())
dfReviews = dfReviews.astype(int)
X = np.array(dfReviews)
tpercent = 0.9
tsize = int(np.floor(tpercent * len(dfReviews)))
dfReviews_train = X[:tsize]
dfReviews_test = X[tsize:]
lentrain = len(dfReviews_train)
lentest = len(dfReviews_test)
df5_train_target = data_meta_2['overall'][:lentrain]
df5_test_target = data_meta_2['overall'][lentrain:lentrain + lentest]
df5_train_target = df5_train_target.astype(int)
df5_test_target = df5_test_target.astype(int)
n_neighbors = 3
knnclf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
knnclf.fit(dfReviews_train, df5_train_target)
knnpreds_test = knnclf.predict(dfReviews_test)
df5_train_target = data_meta_2['overall'][:lentrain]
df5_test_target = data_meta_2['overall'][lentrain:lentrain + lentest]
df5_train_target = df5_train_target.astype(int)
df5_test_target = df5_test_target.astype(int)
n_neighbors = 28
knnclf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
knnclf.fit(dfReviews_train, df5_train_target)
knnpreds_test = knnclf.predict(dfReviews_test)
print(accuracy_score(df5_test_target, knnpreds_test)) | code |
128029150/cell_26 | [
"text_plain_output_1.png"
] | from pandas import DataFrame
from sklearn import neighbors
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics import classification_report
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_meta[['asin', 'overall', 'unixReviewTime']]
data_meta_1
data_meta_2 = data_meta[['asin', 'overall', 'summaryRev']]
data_meta_2
regEx = re.compile('[^a-z]+')
def cleanReviews(reviewText):
reviewText = reviewText.lower()
reviewText = regEx.sub(' ', reviewText).strip()
return reviewText
data_meta_2['summaryClean'] = data_meta_2['summaryRev'].apply(cleanReviews)
data_meta_2 = data_meta_2.reset_index()
reviews = data_meta_2['summaryClean']
countVector = CountVectorizer(max_features=300, stop_words='english')
transformedReviews = countVector.fit_transform(reviews)
dfReviews = DataFrame(transformedReviews.A, columns=countVector.get_feature_names())
dfReviews = dfReviews.astype(int)
X = np.array(dfReviews)
tpercent = 0.9
tsize = int(np.floor(tpercent * len(dfReviews)))
dfReviews_train = X[:tsize]
dfReviews_test = X[tsize:]
lentrain = len(dfReviews_train)
lentest = len(dfReviews_test)
df5_train_target = data_meta_2['overall'][:lentrain]
df5_test_target = data_meta_2['overall'][lentrain:lentrain + lentest]
df5_train_target = df5_train_target.astype(int)
df5_test_target = df5_test_target.astype(int)
n_neighbors = 3
knnclf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
knnclf.fit(dfReviews_train, df5_train_target)
knnpreds_test = knnclf.predict(dfReviews_test)
df5_train_target = data_meta_2['overall'][:lentrain]
df5_test_target = data_meta_2['overall'][lentrain:lentrain + lentest]
df5_train_target = df5_train_target.astype(int)
df5_test_target = df5_test_target.astype(int)
n_neighbors = 28
knnclf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
knnclf.fit(dfReviews_train, df5_train_target)
knnpreds_test = knnclf.predict(dfReviews_test)
X = np.array(dfReviews)
tpercent = 0.85
tsize = int(np.floor(tpercent * len(dfReviews)))
dfReviews_train = X[:tsize]
dfReviews_test = X[tsize:]
lentrain = len(dfReviews_train)
lentest = len(dfReviews_test)
df5_train_target = data_meta_2['overall'][:lentrain]
df5_test_target = data_meta_2['overall'][lentrain:lentrain + lentest]
df5_train_target = df5_train_target.astype(int)
df5_test_target = df5_test_target.astype(int)
n_neighbors = 7
knnclf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
knnclf.fit(dfReviews_train, df5_train_target)
knnpreds_test = knnclf.predict(dfReviews_test)
print(classification_report(df5_test_target, knnpreds_test)) | code |
128029150/cell_11 | [
"text_html_output_1.png"
] | from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_meta[['asin', 'overall', 'unixReviewTime']]
data_meta_1
data_meta_2 = data_meta[['asin', 'overall', 'summaryRev']]
data_meta_2
regEx = re.compile('[^a-z]+')
def cleanReviews(reviewText):
reviewText = reviewText.lower()
reviewText = regEx.sub(' ', reviewText).strip()
return reviewText
data_meta_2['summaryClean'] = data_meta_2['summaryRev'].apply(cleanReviews)
data_meta_2 = data_meta_2.reset_index()
reviews = data_meta_2['summaryClean']
countVector = CountVectorizer(max_features=300, stop_words='english')
transformedReviews = countVector.fit_transform(reviews)
dfReviews = DataFrame(transformedReviews.A, columns=countVector.get_feature_names())
dfReviews = dfReviews.astype(int)
dfReviews | code |
128029150/cell_19 | [
"text_plain_output_1.png"
] | from pandas import DataFrame
from sklearn import neighbors
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics import classification_report
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_meta[['asin', 'overall', 'unixReviewTime']]
data_meta_1
data_meta_2 = data_meta[['asin', 'overall', 'summaryRev']]
data_meta_2
regEx = re.compile('[^a-z]+')
def cleanReviews(reviewText):
reviewText = reviewText.lower()
reviewText = regEx.sub(' ', reviewText).strip()
return reviewText
data_meta_2['summaryClean'] = data_meta_2['summaryRev'].apply(cleanReviews)
data_meta_2 = data_meta_2.reset_index()
reviews = data_meta_2['summaryClean']
countVector = CountVectorizer(max_features=300, stop_words='english')
transformedReviews = countVector.fit_transform(reviews)
dfReviews = DataFrame(transformedReviews.A, columns=countVector.get_feature_names())
dfReviews = dfReviews.astype(int)
X = np.array(dfReviews)
tpercent = 0.9
tsize = int(np.floor(tpercent * len(dfReviews)))
dfReviews_train = X[:tsize]
dfReviews_test = X[tsize:]
lentrain = len(dfReviews_train)
lentest = len(dfReviews_test)
df5_train_target = data_meta_2['overall'][:lentrain]
df5_test_target = data_meta_2['overall'][lentrain:lentrain + lentest]
df5_train_target = df5_train_target.astype(int)
df5_test_target = df5_test_target.astype(int)
n_neighbors = 3
knnclf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
knnclf.fit(dfReviews_train, df5_train_target)
knnpreds_test = knnclf.predict(dfReviews_test)
df5_train_target = data_meta_2['overall'][:lentrain]
df5_test_target = data_meta_2['overall'][lentrain:lentrain + lentest]
df5_train_target = df5_train_target.astype(int)
df5_test_target = df5_test_target.astype(int)
n_neighbors = 28
knnclf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
knnclf.fit(dfReviews_train, df5_train_target)
knnpreds_test = knnclf.predict(dfReviews_test)
print(classification_report(df5_test_target, knnpreds_test)) | code |
128029150/cell_18 | [
"text_html_output_1.png"
] | from pandas import DataFrame
from sklearn import neighbors
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics import classification_report
from sklearn.metrics import mean_squared_error
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_meta[['asin', 'overall', 'unixReviewTime']]
data_meta_1
data_meta_2 = data_meta[['asin', 'overall', 'summaryRev']]
data_meta_2
regEx = re.compile('[^a-z]+')
def cleanReviews(reviewText):
reviewText = reviewText.lower()
reviewText = regEx.sub(' ', reviewText).strip()
return reviewText
data_meta_2['summaryClean'] = data_meta_2['summaryRev'].apply(cleanReviews)
data_meta_2 = data_meta_2.reset_index()
reviews = data_meta_2['summaryClean']
countVector = CountVectorizer(max_features=300, stop_words='english')
transformedReviews = countVector.fit_transform(reviews)
dfReviews = DataFrame(transformedReviews.A, columns=countVector.get_feature_names())
dfReviews = dfReviews.astype(int)
X = np.array(dfReviews)
tpercent = 0.9
tsize = int(np.floor(tpercent * len(dfReviews)))
dfReviews_train = X[:tsize]
dfReviews_test = X[tsize:]
lentrain = len(dfReviews_train)
lentest = len(dfReviews_test)
df5_train_target = data_meta_2['overall'][:lentrain]
df5_test_target = data_meta_2['overall'][lentrain:lentrain + lentest]
df5_train_target = df5_train_target.astype(int)
df5_test_target = df5_test_target.astype(int)
n_neighbors = 3
knnclf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
knnclf.fit(dfReviews_train, df5_train_target)
knnpreds_test = knnclf.predict(dfReviews_test)
print(mean_squared_error(df5_test_target, knnpreds_test)) | code |
128029150/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_meta[['asin', 'overall', 'unixReviewTime']]
data_meta_1
data_meta_2 = data_meta[['asin', 'overall', 'summaryRev']]
data_meta_2
regEx = re.compile('[^a-z]+')
def cleanReviews(reviewText):
reviewText = reviewText.lower()
reviewText = regEx.sub(' ', reviewText).strip()
return reviewText
data_meta_2['summaryClean'] = data_meta_2['summaryRev'].apply(cleanReviews)
data_meta_2 = data_meta_2.reset_index() | code |
128029150/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.neighbors import NearestNeighbors
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_meta[['asin', 'overall', 'unixReviewTime']]
data_meta_1
data_meta_2 = data_meta[['asin', 'overall', 'summaryRev']]
data_meta_2
regEx = re.compile('[^a-z]+')
def cleanReviews(reviewText):
reviewText = reviewText.lower()
reviewText = regEx.sub(' ', reviewText).strip()
return reviewText
data_meta_2['summaryClean'] = data_meta_2['summaryRev'].apply(cleanReviews)
data_meta_2 = data_meta_2.reset_index()
reviews = data_meta_2['summaryClean']
countVector = CountVectorizer(max_features=300, stop_words='english')
transformedReviews = countVector.fit_transform(reviews)
dfReviews = DataFrame(transformedReviews.A, columns=countVector.get_feature_names())
dfReviews = dfReviews.astype(int)
X = np.array(dfReviews)
tpercent = 0.9
tsize = int(np.floor(tpercent * len(dfReviews)))
dfReviews_train = X[:tsize]
dfReviews_test = X[tsize:]
lentrain = len(dfReviews_train)
lentest = len(dfReviews_test)
neighbor = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(dfReviews_train)
distances, indices = neighbor.kneighbors(dfReviews_train)
for i in range(lentest):
a = neighbor.kneighbors([dfReviews_test[i]])
related_product_list = a[1]
first_related_product = [item[0] for item in related_product_list]
first_related_product = str(first_related_product).strip('[]')
first_related_product = int(first_related_product)
second_related_product = [item[1] for item in related_product_list]
second_related_product = str(second_related_product).strip('[]')
second_related_product = int(second_related_product)
print('Based on product reviews, for ', data_meta_2['asin'][lentrain + i], ' average rating is ', data_meta_2['overall'][lentrain + i])
print('The first similar product is ', data_meta_2['asin'][first_related_product], ' average rating is ', data_meta_2['overall'][first_related_product])
print('The second similar product is ', data_meta_2['asin'][second_related_product], ' average rating is ', data_meta_2['overall'][second_related_product])
print('-----------------------------------------------------------') | code |
128029150/cell_16 | [
"text_html_output_1.png"
] | from pandas import DataFrame
from sklearn import neighbors
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics import classification_report
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_meta[['asin', 'overall', 'unixReviewTime']]
data_meta_1
data_meta_2 = data_meta[['asin', 'overall', 'summaryRev']]
data_meta_2
regEx = re.compile('[^a-z]+')
def cleanReviews(reviewText):
reviewText = reviewText.lower()
reviewText = regEx.sub(' ', reviewText).strip()
return reviewText
data_meta_2['summaryClean'] = data_meta_2['summaryRev'].apply(cleanReviews)
data_meta_2 = data_meta_2.reset_index()
reviews = data_meta_2['summaryClean']
countVector = CountVectorizer(max_features=300, stop_words='english')
transformedReviews = countVector.fit_transform(reviews)
dfReviews = DataFrame(transformedReviews.A, columns=countVector.get_feature_names())
dfReviews = dfReviews.astype(int)
X = np.array(dfReviews)
tpercent = 0.9
tsize = int(np.floor(tpercent * len(dfReviews)))
dfReviews_train = X[:tsize]
dfReviews_test = X[tsize:]
lentrain = len(dfReviews_train)
lentest = len(dfReviews_test)
df5_train_target = data_meta_2['overall'][:lentrain]
df5_test_target = data_meta_2['overall'][lentrain:lentrain + lentest]
df5_train_target = df5_train_target.astype(int)
df5_test_target = df5_test_target.astype(int)
n_neighbors = 3
knnclf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
knnclf.fit(dfReviews_train, df5_train_target)
knnpreds_test = knnclf.predict(dfReviews_test)
print(classification_report(df5_test_target, knnpreds_test)) | code |
128029150/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta | code |
128029150/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pandas import DataFrame
from sklearn import neighbors
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_meta[['asin', 'overall', 'unixReviewTime']]
data_meta_1
data_meta_2 = data_meta[['asin', 'overall', 'summaryRev']]
data_meta_2
regEx = re.compile('[^a-z]+')
def cleanReviews(reviewText):
reviewText = reviewText.lower()
reviewText = regEx.sub(' ', reviewText).strip()
return reviewText
data_meta_2['summaryClean'] = data_meta_2['summaryRev'].apply(cleanReviews)
data_meta_2 = data_meta_2.reset_index()
reviews = data_meta_2['summaryClean']
countVector = CountVectorizer(max_features=300, stop_words='english')
transformedReviews = countVector.fit_transform(reviews)
dfReviews = DataFrame(transformedReviews.A, columns=countVector.get_feature_names())
dfReviews = dfReviews.astype(int)
X = np.array(dfReviews)
tpercent = 0.9
tsize = int(np.floor(tpercent * len(dfReviews)))
dfReviews_train = X[:tsize]
dfReviews_test = X[tsize:]
lentrain = len(dfReviews_train)
lentest = len(dfReviews_test)
df5_train_target = data_meta_2['overall'][:lentrain]
df5_test_target = data_meta_2['overall'][lentrain:lentrain + lentest]
df5_train_target = df5_train_target.astype(int)
df5_test_target = df5_test_target.astype(int)
n_neighbors = 3
knnclf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
knnclf.fit(dfReviews_train, df5_train_target)
knnpreds_test = knnclf.predict(dfReviews_test)
print(accuracy_score(df5_test_target, knnpreds_test)) | code |
128029150/cell_10 | [
"text_html_output_1.png"
] | from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_meta[['asin', 'overall', 'unixReviewTime']]
data_meta_1
data_meta_2 = data_meta[['asin', 'overall', 'summaryRev']]
data_meta_2
regEx = re.compile('[^a-z]+')
def cleanReviews(reviewText):
reviewText = reviewText.lower()
reviewText = regEx.sub(' ', reviewText).strip()
return reviewText
data_meta_2['summaryClean'] = data_meta_2['summaryRev'].apply(cleanReviews)
data_meta_2 = data_meta_2.reset_index()
reviews = data_meta_2['summaryClean']
countVector = CountVectorizer(max_features=300, stop_words='english')
transformedReviews = countVector.fit_transform(reviews)
dfReviews = DataFrame(transformedReviews.A, columns=countVector.get_feature_names())
dfReviews = dfReviews.astype(int) | code |
128029150/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'") | code |
16135360/cell_42 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df.popmale.sum()
df.popfemale.sum()
df[df.popmale == df.popmale.max()]
df[df.popfemale == df.popfemale.max()]
df.columns
lit_states = df.groupby('state').agg({'litertotal': np.sum})
popstates = df.groupby('state').agg({'poptotal': np.sum})
literate_rate = lit_states.litertotal * 100 / popstates.poptotal
literate_rate = literate_rate.sort_values(ascending=False)
df[['city', 'sexratio']].sort_values(by='sexratio', ascending=False).head(10) | code |
16135360/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
population_state = df[['state', 'poptotal']].groupby('state').sum().sort_values(by='poptotal', ascending=False)
population_state.head(10) | code |
16135360/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique() | code |
16135360/cell_25 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df[['city', 'poptotal']].sort_values(by='poptotal', ascending=False).tail(10) | code |
16135360/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum() | code |
16135360/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df.popmale.sum()
df.popfemale.sum()
df[df.popmale == df.popmale.max()]
df[df.popfemale == df.popfemale.max()]
df.columns | code |
16135360/cell_23 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
dc = df[['state', 'dcode']].groupby(['state']).count()
dc = dc.sort_values(by='dcode', ascending=False)
dc
population_state = df[['state', 'poptotal']].groupby('state').sum().sort_values(by='poptotal', ascending=False)
plt.figure(figsize=[14, 10])
sns.barplot(y=population_state.index, x=population_state.poptotal)
plt.title('States according to Total Population', fontsize=25)
plt.xlabel('Population', fontsize=20)
plt.ylabel('State', fontsize=20)
plt.show() | code |
16135360/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df.popmale.sum()
df.popfemale.sum()
df[df.popmale == df.popmale.max()] | code |
16135360/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df.popmale.sum()
df.popfemale.sum()
df[df.popmale == df.popmale.max()]
df[df.popfemale == df.popfemale.max()]
df[['city', '0-6poptotal']].sort_values(by='0-6poptotal', ascending=False).tail(10) | code |
16135360/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T | code |
16135360/cell_40 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
dc = df[['state', 'dcode']].groupby(['state']).count()
dc = dc.sort_values(by='dcode', ascending=False)
dc
population_state = df[['state', 'poptotal']].groupby('state').sum().sort_values(by='poptotal', ascending=False)
df.popmale.sum()
df.popfemale.sum()
df[df.popmale == df.popmale.max()]
df[df.popfemale == df.popfemale.max()]
df.columns
lit_states = df.groupby('state').agg({'litertotal': np.sum})
popstates = df.groupby('state').agg({'poptotal': np.sum})
literate_rate = lit_states.litertotal * 100 / popstates.poptotal
literate_rate = literate_rate.sort_values(ascending=False)
plt.figure(figsize=[14, 10])
sns.barplot(x=literate_rate, y=literate_rate.index)
plt.title('States according to literacy rate', fontsize=25)
plt.xlabel('Literacy Rate', fontsize=20)
plt.ylabel('State', fontsize=20)
plt.show() | code |
16135360/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df.popmale.sum()
df.popfemale.sum()
df['0-6popfemale'].sum() | code |
16135360/cell_39 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df.popmale.sum()
df.popfemale.sum()
df[df.popmale == df.popmale.max()]
df[df.popfemale == df.popfemale.max()]
df.columns
lit_states = df.groupby('state').agg({'litertotal': np.sum})
popstates = df.groupby('state').agg({'poptotal': np.sum})
literate_rate = lit_states.litertotal * 100 / popstates.poptotal
literate_rate = literate_rate.sort_values(ascending=False)
literate_rate | code |
16135360/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df.popmale.sum() | code |
16135360/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T | code |
16135360/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T | code |
16135360/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
dc = df[['state', 'dcode']].groupby(['state']).count()
dc = dc.sort_values(by='dcode', ascending=False)
dc
plt.figure(figsize=[14, 10])
sns.barplot(y=dc.index, x=dc.dcode)
plt.title('Number of Districts in each States Taken', fontsize=25)
plt.ylabel('State', fontsize=20)
plt.xlabel('No. of Districts', fontsize=20)
plt.show() | code |
16135360/cell_1 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input')) | code |
16135360/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
city_pop = df['poptotal'].sum()
total_indian_pop = 1247200000
percentage = city_pop / total_indian_pop * 100
print('{0:.1f}% of population living in cities'.format(percentage)) | code |
16135360/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
dc = df[['state', 'dcode']].groupby(['state']).count()
dc = dc.sort_values(by='dcode', ascending=False)
print('No. of districts present in each state')
dc | code |
16135360/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df.popmale.sum()
df.popfemale.sum()
df[df.popmale == df.popmale.max()]
df[df.popfemale == df.popfemale.max()]
df[['city', '0-6poptotal']].sort_values(by='0-6poptotal', ascending=False).head(10) | code |
16135360/cell_28 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df.popmale.sum()
df.popfemale.sum()
df['0-6popmale'].sum() | code |
16135360/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df['gradratio'] = df['totalgrad'] / df['poptotal']
grad_avg = df['gradratio'].mean()
print('Graduate Ratio in Indian Cities: {0:.2f}'.format(grad_avg)) | code |
16135360/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
plt.figure(figsize=(12, 8))
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
city_count.plot(kind='barh', fontsize=10, width=1, edgecolor='black')
plt.xlabel('No of cities', fontsize=15)
plt.ylabel('States', fontsize=15)
plt.title('Count of Cities taken from each State', fontsize=20)
plt.show() | code |
16135360/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
sc = df[['state', 'scode']].groupby(['state', 'scode']).count()
print('State codes')
sc | code |
16135360/cell_43 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df.popmale.sum()
df.popfemale.sum()
df[df.popmale == df.popmale.max()]
df[df.popfemale == df.popfemale.max()]
df.columns
lit_states = df.groupby('state').agg({'litertotal': np.sum})
popstates = df.groupby('state').agg({'poptotal': np.sum})
literate_rate = lit_states.litertotal * 100 / popstates.poptotal
literate_rate = literate_rate.sort_values(ascending=False)
df[['city', 'sexratio']].sort_values(by='sexratio', ascending=False).tail(10) | code |
16135360/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df.popmale.sum()
df.popfemale.sum()
df[df.popmale == df.popmale.max()]
df[df.popfemale == df.popfemale.max()] | code |
16135360/cell_24 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df[['city', 'poptotal']].sort_values(by='poptotal', ascending=False).head(10) | code |
16135360/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count() | code |
16135360/cell_22 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
population_state = df[['state', 'poptotal']].groupby('state').sum().sort_values(by='poptotal', ascending=False)
population_state.tail(10) | code |
16135360/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df.popmale.sum()
df.popfemale.sum() | code |
16135360/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df.popmale.sum()
df.popfemale.sum()
df[df.popmale == df.popmale.max()]
df[df.popfemale == df.popfemale.max()]
df.columns
df[['city', 'elrtotal']].sort_values(by='elrtotal', ascending=False).tail(10) | code |
16135360/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.info() | code |
16135360/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'location', 'totalgrad', 'malegrad', 'femalegrad']
df.isnull().sum()
df.describe().T
df = df.drop('location', axis=1)
df.head().T
df.state.nunique()
df.city.count()
city_count = df.groupby('state')['city'].count().sort_values(ascending=True)
df.popmale.sum()
df.popfemale.sum()
df[df.popmale == df.popmale.max()]
df[df.popfemale == df.popfemale.max()]
df.columns
df[['city', 'elrtotal']].sort_values(by='elrtotal', ascending=False).head(10) | code |
16166088/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
_data = pd.read_csv('../input/zomato.csv')
data = _data.drop(columns=['url', 'address', 'phone'], axis=1)
columns = data.columns
splist = []
cuisine = []
for i in range(0, data['cuisines'].count()):
splist = str(data['cuisines'][i]).split(', ')
for item in splist:
if item not in cuisine:
cuisine.append(item)
cuisine | code |
16166088/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
_data = pd.read_csv('../input/zomato.csv')
data = _data.drop(columns=['url', 'address', 'phone'], axis=1)
columns = data.columns
splist = []
cuisine = []
for i in range(0, data['cuisines'].count()):
splist = str(data['cuisines'][i]).split(', ')
for item in splist:
if item not in cuisine:
cuisine.append(item)
cuisine
cuisineCount = pd.DataFrame(columns=['cuisines', 'count'])
i = 0
for c in cuisine:
restaurant = data['cuisines'].str.contains(c, case=False, regex=True, na=False)
cuisineCount.loc[i] = [c, restaurant[restaurant == True].count()]
i = i + 1
cuisineCount.sort_values(by='count', axis=0, ascending=False, inplace=True)
import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(1, figsize=(30, 15))
data_CuisineCount = cuisineCount.iloc[0:25, :]
plt.subplot(2, 1, 1)
plt.subplots_adjust(hspace=0.5, wspace=0.5)
plt.bar(data_CuisineCount['cuisines'], data_CuisineCount['count'])
plt.subplot(2, 1, 2)
plt.subplots_adjust(hspace=0.5, wspace=0.5)
plt.pie(x=data_CuisineCount['count'], labels=data_CuisineCount['cuisines'])
plt.show() | code |
16166088/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
_data = pd.read_csv('../input/zomato.csv')
_data.head() | code |
16166088/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16166088/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
_data = pd.read_csv('../input/zomato.csv')
print('Original set of columns:{}'.format(_data.columns))
data = _data.drop(columns=['url', 'address', 'phone'], axis=1)
columns = data.columns
print('New columns : {}'.format(columns)) | code |
16166088/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
_data = pd.read_csv('../input/zomato.csv')
data = _data.drop(columns=['url', 'address', 'phone'], axis=1)
columns = data.columns
splist = []
cuisine = []
for i in range(0, data['cuisines'].count()):
splist = str(data['cuisines'][i]).split(', ')
for item in splist:
if item not in cuisine:
cuisine.append(item)
cuisine
cuisineCount = pd.DataFrame(columns=['cuisines', 'count'])
i = 0
for c in cuisine:
restaurant = data['cuisines'].str.contains(c, case=False, regex=True, na=False)
cuisineCount.loc[i] = [c, restaurant[restaurant == True].count()]
i = i + 1
cuisineCount.sort_values(by='count', axis=0, ascending=False, inplace=True)
print('The top 10 cuisines sold in bangalore:\n{}'.format(cuisineCount.head(25))) | code |
105212943/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes
features = df.keys()
features = features.drop('churn')
subsets = ['credit_score']
df.groupby('churn')[features].mean().style.background_gradient(cmap='ocean')
fig = px.histogram(df, x="age", y="balance", color="churn",
marginal="box",
hover_data=df.columns)
fig.show()
sizes = [df.churn[df['churn']==1].count(), df.churn[df['churn']==0].count()]
labels = ['Churned', 'Not Churned']
figure, axes = plt.subplots(figsize=(10, 8))
axes.pie(sizes, labels=labels,shadow=True,autopct = '%1.2f%%')
plt.legend()
plt.title("Churned VS Not Churned", size = 15)
plt.show()
churned_french = df.churn[(df.country == 'France') & (df.churn == 1)].count()
count_french = df.churn[df.country == 'France'].count()
churned_german = df.churn[(df.country == 'Germany') & (df.churn == 1)].count()
count_german = df.churn[df.country == 'Germany'].count()
churned_spain = df.churn[(df.country == 'Spain') & (df.churn == 1)].count()
count_spain = df.churn[df.country == 'Spain'].count()
px.histogram(df, x='country', color='churn', barmode='group') | code |
105212943/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes
features = df.keys()
features = features.drop('churn')
subsets = ['credit_score']
df.groupby('churn')[features].mean().style.background_gradient(cmap='ocean') | code |
105212943/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape | code |
105212943/cell_2 | [
"text_plain_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import missingno as msno
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler,LabelEncoder
import scipy.special
import scipy.stats as stats
from scipy.stats import skew, kurtosis, shapiro
!pip install --pre --quiet pycaret
from pycaret.classification import *
import warnings
warnings.filterwarnings('ignore') | code |
105212943/cell_11 | [
"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 plotly.express as px
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes
features = df.keys()
features = features.drop('churn')
subsets = ['credit_score']
df.groupby('churn')[features].mean().style.background_gradient(cmap='ocean')
fig = px.histogram(df, x="age", y="balance", color="churn",
marginal="box",
hover_data=df.columns)
fig.show()
sizes = [df.churn[df['churn'] == 1].count(), df.churn[df['churn'] == 0].count()]
labels = ['Churned', 'Not Churned']
figure, axes = plt.subplots(figsize=(10, 8))
axes.pie(sizes, labels=labels, shadow=True, autopct='%1.2f%%')
plt.legend()
plt.title('Churned VS Not Churned', size=15)
plt.show() | code |
105212943/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 |
105212943/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe | code |
105212943/cell_18 | [
"text_html_output_2.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import seaborn as sns
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes
features = df.keys()
features = features.drop('churn')
subsets = ['credit_score']
df.groupby('churn')[features].mean().style.background_gradient(cmap='ocean')
fig = px.histogram(df, x="age", y="balance", color="churn",
marginal="box",
hover_data=df.columns)
fig.show()
sizes = [df.churn[df['churn']==1].count(), df.churn[df['churn']==0].count()]
labels = ['Churned', 'Not Churned']
figure, axes = plt.subplots(figsize=(10, 8))
axes.pie(sizes, labels=labels,shadow=True,autopct = '%1.2f%%')
plt.legend()
plt.title("Churned VS Not Churned", size = 15)
plt.show()
churned_french = df.churn[(df.country == 'France') & (df.churn == 1)].count()
count_french = df.churn[df.country == 'France'].count()
churned_german = df.churn[(df.country == 'Germany') & (df.churn == 1)].count()
count_german = df.churn[df.country == 'Germany'].count()
churned_spain = df.churn[(df.country == 'Spain') & (df.churn == 1)].count()
count_spain = df.churn[df.country == 'Spain'].count()
from collections import Counter
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
df.loc[detect_outliers(df, ['credit_score', 'balance', 'age', 'estimated_salary'])]
plt.subplots(figsize=(20, 10))
sns.heatmap(df.corr(), annot=True, fmt='.2f', cmap='viridis') | code |
105212943/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes | code |
105212943/cell_17 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes
features = df.keys()
features = features.drop('churn')
subsets = ['credit_score']
df.groupby('churn')[features].mean().style.background_gradient(cmap='ocean')
fig = px.histogram(df, x="age", y="balance", color="churn",
marginal="box",
hover_data=df.columns)
fig.show()
sizes = [df.churn[df['churn']==1].count(), df.churn[df['churn']==0].count()]
labels = ['Churned', 'Not Churned']
figure, axes = plt.subplots(figsize=(10, 8))
axes.pie(sizes, labels=labels,shadow=True,autopct = '%1.2f%%')
plt.legend()
plt.title("Churned VS Not Churned", size = 15)
plt.show()
churned_french = df.churn[(df.country == 'France') & (df.churn == 1)].count()
count_french = df.churn[df.country == 'France'].count()
churned_german = df.churn[(df.country == 'Germany') & (df.churn == 1)].count()
count_german = df.churn[df.country == 'Germany'].count()
churned_spain = df.churn[(df.country == 'Spain') & (df.churn == 1)].count()
count_spain = df.churn[df.country == 'Spain'].count()
from collections import Counter
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
df.loc[detect_outliers(df, ['credit_score', 'balance', 'age', 'estimated_salary'])] | code |
105212943/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes
features = df.keys()
features = features.drop('churn')
subsets = ['credit_score']
df.groupby('churn')[features].mean().style.background_gradient(cmap='ocean')
fig = px.histogram(df, x="age", y="balance", color="churn",
marginal="box",
hover_data=df.columns)
fig.show()
sizes = [df.churn[df['churn']==1].count(), df.churn[df['churn']==0].count()]
labels = ['Churned', 'Not Churned']
figure, axes = plt.subplots(figsize=(10, 8))
axes.pie(sizes, labels=labels,shadow=True,autopct = '%1.2f%%')
plt.legend()
plt.title("Churned VS Not Churned", size = 15)
plt.show()
churned_french = df.churn[(df.country == 'France') & (df.churn == 1)].count()
count_french = df.churn[df.country == 'France'].count()
churned_german = df.churn[(df.country == 'Germany') & (df.churn == 1)].count()
count_german = df.churn[df.country == 'Germany'].count()
churned_spain = df.churn[(df.country == 'Spain') & (df.churn == 1)].count()
count_spain = df.churn[df.country == 'Spain'].count()
for col in df[['country', 'gender', 'products_number', 'credit_card', 'active_member', 'churn']]:
print('******************')
print(col)
print('******************')
print(df[col].value_counts(dropna=False, normalize=True))
print('_____________________________________________________') | code |
105212943/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes
features = df.keys()
features = features.drop('churn')
subsets = ['credit_score']
df.groupby('churn')[features].mean().style.background_gradient(cmap='ocean')
fig = px.histogram(df, x='age', y='balance', color='churn', marginal='box', hover_data=df.columns)
fig.show() | code |
105212943/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes
features = df.keys()
features = features.drop('churn')
subsets = ['credit_score']
df.groupby('churn')[features].mean().style.background_gradient(cmap='ocean')
fig = px.histogram(df, x="age", y="balance", color="churn",
marginal="box",
hover_data=df.columns)
fig.show()
sizes = [df.churn[df['churn']==1].count(), df.churn[df['churn']==0].count()]
labels = ['Churned', 'Not Churned']
figure, axes = plt.subplots(figsize=(10, 8))
axes.pie(sizes, labels=labels,shadow=True,autopct = '%1.2f%%')
plt.legend()
plt.title("Churned VS Not Churned", size = 15)
plt.show()
churned_french = df.churn[(df.country == 'France') & (df.churn == 1)].count()
count_french = df.churn[df.country == 'France'].count()
print('Percent of French People Who Churned --->', churned_french * 100 / count_french, '%')
churned_german = df.churn[(df.country == 'Germany') & (df.churn == 1)].count()
count_german = df.churn[df.country == 'Germany'].count()
print('Percent of German People Who Churned --->', churned_german * 100 / count_german, '%')
churned_spain = df.churn[(df.country == 'Spain') & (df.churn == 1)].count()
count_spain = df.churn[df.country == 'Spain'].count()
print('Percent of Spanish People Who Churned --->', churned_spain * 100 / count_spain, '%') | code |
105212943/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.head() | code |
49129186/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 |
49129186/cell_3 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv', header=1, na_values='NaN')
data = data.fillna('Not Given')
data.head() | code |
49129186/cell_12 | [
"text_html_output_1.png"
] | from IPython.display import display,clear_output
from ipywidgets import interact, interactive, fixed, interact_manual,VBox,HBox,Layout
import ipywidgets as widgets
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv', header=1, na_values='NaN')
data = data.fillna('Not Given')
def plot_map(grp_data):
fig = go.Figure(data=go.Choropleth(locations=grp_data['Country'], z=grp_data['people'], text=grp_data['Country'], colorscale='earth', locationmode='country names', autocolorscale=False, reversescale=False, marker_line_color='darkgray', marker_line_width=0.5, colorbar_tickprefix='', colorbar_title='people'))
fig.update_layout(title_text='People by country', height=500, width=500, geo=dict(showframe=False, showcoastlines=False, projection_type='equirectangular'), annotations=[dict(x=0.55, y=0.1, xref='paper', yref='paper', showarrow=False)])
def map_gender_plot(df):
gender = data['What is your gender? - Selected Choice'].unique().tolist()
xvar = widgets.Dropdown(options=gender, description='Gender:')
def f(xvar_):
grp_data = data[data['What is your gender? - Selected Choice'] == xvar_].groupby(by='In which country do you currently reside?').size().sort_values(ascending=False).reset_index()
grp_data.columns = ['Country', 'people']
a = interactive(f, xvar_=xvar)
ctrl_map = HBox(a.children[:-1], layout=Layout(flex_flow='row wrap'))
out_map = a.children[-1]
return [ctrl_map, out_map]
def map_age_plot(df):
age = data['What is your age (# years)?'].sort_values().unique().tolist()
xvar = widgets.Dropdown(options=age, description='Age:')
def f(xvar_):
grp_data = data[data['What is your age (# years)?'] == xvar_].groupby(by='In which country do you currently reside?').size().sort_values(ascending=False).reset_index()
grp_data.columns = ['Country', 'people']
a = interactive(f, xvar_=xvar)
ctrl_map = HBox(a.children[:-1], layout=Layout(flex_flow='row wrap'))
out_map = a.children[-1]
return [ctrl_map, out_map]
def map_education_plot(df):
age = data['What is the highest level of formal education that you have attained or plan to attain within the next 2 years?'].sort_values().unique().tolist()
xvar = widgets.Dropdown(options=age, description='Education:')
def f(xvar_):
grp_data = data[data['What is the highest level of formal education that you have attained or plan to attain within the next 2 years?'] == xvar_].groupby(by='In which country do you currently reside?').size().sort_values(ascending=False).reset_index()
grp_data.columns = ['Country', 'people']
a = interactive(f, xvar_=xvar)
ctrl_map = HBox(a.children[:-1], layout=Layout(flex_flow='row wrap'))
out_map = a.children[-1]
return [ctrl_map, out_map]
def map_role_plot(df):
age = data['Select the title most similar to your current role (or most recent title if retired): - Selected Choice'].sort_values().unique().tolist()
xvar = widgets.Dropdown(options=age, description='Current Role:')
def f(xvar_):
grp_data = data[data['Select the title most similar to your current role (or most recent title if retired): - Selected Choice'] == xvar_].groupby(by='In which country do you currently reside?').size().sort_values(ascending=False).reset_index()
grp_data.columns = ['Country', 'people']
a = interactive(f, xvar_=xvar)
ctrl_map = HBox(a.children[:-1], layout=Layout(flex_flow='row wrap'))
out_map = a.children[-1]
return [ctrl_map, out_map]
tab1 = HBox(map_gender_plot(data))
tab2 = HBox(map_age_plot(data))
tab3 = HBox(map_education_plot(data))
tab4 = HBox(map_role_plot(data))
tab = widgets.Tab(children=[tab1, tab2, tab3, tab4])
tab.set_title(0, 'Gender')
tab.set_title(1, 'Age')
tab.set_title(2, 'Education')
tab.set_title(3, 'Role')
display(tab) | code |
18153034/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating + games.black_rating) / 2
games['rating_diff'] = abs(games.white_rating - games.black_rating)
games.victory_status.value_counts()
under_1500 = games[games.mean_rating < 1500]
under_2000 = games[games.mean_rating < 2000]
over_2000 = games[games.mean_rating > 2000]
brackets = [under_1500, under_2000, over_2000]
bracket_titles = ['Under 1500', 'Under 2000', 'Over 2000']
for i, bracket in enumerate(brackets):
victory_status = bracket.victory_status.value_counts()
mate_games = games[games.victory_status == 'mate']
under_1500 = mate_games[mate_games.mean_rating < 1500]
under_2000 = mate_games[mate_games.mean_rating < 2000]
over_2000 = mate_games[mate_games.mean_rating > 2000]
m_brackets = [under_1500, under_2000, over_2000]
turn_means = [b.turns.mean() for b in m_brackets]
plt.ylim(0, 100)
plt.figure(figsize=(10, 5))
plt.scatter(mate_games.mean_rating, mate_games.turns) | code |
18153034/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating + games.black_rating) / 2
games['rating_diff'] = abs(games.white_rating - games.black_rating)
plt.figure(figsize=(10, 5))
sns.distplot(games['mean_rating']) | code |
18153034/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating + games.black_rating) / 2
games['rating_diff'] = abs(games.white_rating - games.black_rating)
games.victory_status.value_counts()
under_1500 = games[games.mean_rating < 1500]
under_2000 = games[games.mean_rating < 2000]
over_2000 = games[games.mean_rating > 2000]
brackets = [under_1500, under_2000, over_2000]
bracket_titles = ['Under 1500', 'Under 2000', 'Over 2000']
for i, bracket in enumerate(brackets):
victory_status = bracket.victory_status.value_counts()
mate_games = games[games.victory_status == 'mate']
under_1500 = mate_games[mate_games.mean_rating < 1500]
under_2000 = mate_games[mate_games.mean_rating < 2000]
over_2000 = mate_games[mate_games.mean_rating > 2000]
m_brackets = [under_1500, under_2000, over_2000]
turn_means = [b.turns.mean() for b in m_brackets]
plt.ylim(0, 100)
mate_games.loc[mate_games['turns'].idxmax()] | code |
18153034/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating + games.black_rating) / 2
games['rating_diff'] = abs(games.white_rating - games.black_rating)
games.victory_status.value_counts()
under_1500 = games[games.mean_rating < 1500]
under_2000 = games[games.mean_rating < 2000]
over_2000 = games[games.mean_rating > 2000]
brackets = [under_1500, under_2000, over_2000]
bracket_titles = ['Under 1500', 'Under 2000', 'Over 2000']
mate_games = games[games.victory_status == 'mate']
under_1500 = mate_games[mate_games.mean_rating < 1500]
under_2000 = mate_games[mate_games.mean_rating < 2000]
over_2000 = mate_games[mate_games.mean_rating > 2000]
m_brackets = [under_1500, under_2000, over_2000]
white_upsets = games[(games.winner == 'white') & (games.white_rating < games.black_rating)]
black_upsets = games[(games.winner == 'black') & (games.black_rating < games.white_rating)]
upsets = pd.concat([white_upsets, black_upsets])
THRESHOLD = 900
STEP = 50
u_percentages = []
print(f'Rating difference : Percentage of wins by weaker player')
for i in range(0 + STEP, THRESHOLD, STEP):
th_upsets = upsets[upsets.rating_diff > i]
th_games = games[games.rating_diff > i]
upsets_percentage = th_upsets.shape[0] / th_games.shape[0] * 100
u_percentages.append([i, upsets_percentage])
print(f'{str(i).ljust(18)}: {upsets_percentage:.2f}%') | code |
18153034/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating + games.black_rating) / 2
games['rating_diff'] = abs(games.white_rating - games.black_rating)
games.victory_status.value_counts()
under_1500 = games[games.mean_rating < 1500]
under_2000 = games[games.mean_rating < 2000]
over_2000 = games[games.mean_rating > 2000]
brackets = [under_1500, under_2000, over_2000]
bracket_titles = ['Under 1500', 'Under 2000', 'Over 2000']
for i, bracket in enumerate(brackets):
victory_status = bracket.victory_status.value_counts()
mate_games = games[games.victory_status == 'mate']
under_1500 = mate_games[mate_games.mean_rating < 1500]
under_2000 = mate_games[mate_games.mean_rating < 2000]
over_2000 = mate_games[mate_games.mean_rating > 2000]
m_brackets = [under_1500, under_2000, over_2000]
turn_means = [b.turns.mean() for b in m_brackets]
plt.figure(figsize=(10, 5))
plt.ylim(0, 100)
plt.title('Number of turns until mate')
plt.plot(bracket_titles, turn_means, 'o-', color='r') | code |
18153034/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
games = pd.read_csv('../input/games.csv')
games.head(2) | code |
18153034/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating + games.black_rating) / 2
games['rating_diff'] = abs(games.white_rating - games.black_rating)
games.victory_status.value_counts()
under_1500 = games[games.mean_rating < 1500]
under_2000 = games[games.mean_rating < 2000]
over_2000 = games[games.mean_rating > 2000]
brackets = [under_1500, under_2000, over_2000]
bracket_titles = ['Under 1500', 'Under 2000', 'Over 2000']
for i, bracket in enumerate(brackets):
victory_status = bracket.victory_status.value_counts()
mate_games = games[games.victory_status == 'mate']
under_1500 = mate_games[mate_games.mean_rating < 1500]
under_2000 = mate_games[mate_games.mean_rating < 2000]
over_2000 = mate_games[mate_games.mean_rating > 2000]
m_brackets = [under_1500, under_2000, over_2000]
turn_means = [b.turns.mean() for b in m_brackets]
plt.ylim(0, 100)
mate_games.loc[mate_games['turns'].idxmax()]
scholar_mates = mate_games[mate_games.turns == 4]
scholar_mates | code |
18153034/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating + games.black_rating) / 2
games['rating_diff'] = abs(games.white_rating - games.black_rating)
plt.figure(figsize=(10, 5))
sns.distplot(games.turns) | code |
18153034/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating + games.black_rating) / 2
games['rating_diff'] = abs(games.white_rating - games.black_rating)
games.victory_status.value_counts()
under_1500 = games[games.mean_rating < 1500]
under_2000 = games[games.mean_rating < 2000]
over_2000 = games[games.mean_rating > 2000]
brackets = [under_1500, under_2000, over_2000]
bracket_titles = ['Under 1500', 'Under 2000', 'Over 2000']
plt.figure(figsize=(15, 11))
for i, bracket in enumerate(brackets):
victory_status = bracket.victory_status.value_counts()
plt.subplot(1, 4, i + 1)
plt.title(bracket_titles[i])
plt.pie(victory_status, labels=victory_status.index) | code |
18153034/cell_31 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating + games.black_rating) / 2
games['rating_diff'] = abs(games.white_rating - games.black_rating)
games.victory_status.value_counts()
under_1500 = games[games.mean_rating < 1500]
under_2000 = games[games.mean_rating < 2000]
over_2000 = games[games.mean_rating > 2000]
brackets = [under_1500, under_2000, over_2000]
bracket_titles = ['Under 1500', 'Under 2000', 'Over 2000']
for i, bracket in enumerate(brackets):
victory_status = bracket.victory_status.value_counts()
mate_games = games[games.victory_status == 'mate']
under_1500 = mate_games[mate_games.mean_rating < 1500]
under_2000 = mate_games[mate_games.mean_rating < 2000]
over_2000 = mate_games[mate_games.mean_rating > 2000]
m_brackets = [under_1500, under_2000, over_2000]
turn_means = [b.turns.mean() for b in m_brackets]
plt.ylim(0, 100)
white_upsets = games[(games.winner == 'white') & (games.white_rating < games.black_rating)]
black_upsets = games[(games.winner == 'black') & (games.black_rating < games.white_rating)]
upsets = pd.concat([white_upsets, black_upsets])
THRESHOLD = 900
STEP = 50
u_percentages = []
for i in range(0 + STEP, THRESHOLD, STEP):
th_upsets = upsets[upsets.rating_diff > i]
th_games = games[games.rating_diff > i]
upsets_percentage = th_upsets.shape[0] / th_games.shape[0] * 100
u_percentages.append([i, upsets_percentage])
plt.figure(figsize=(10, 5))
plt.plot(*zip(*u_percentages))
plt.xlabel('rating difference')
plt.ylabel('upsets percentage') | code |
18153034/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating + games.black_rating) / 2
games['rating_diff'] = abs(games.white_rating - games.black_rating)
games.victory_status.value_counts() | code |
122262213/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_text
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
prediction = clf.predict(X_test)
accuracy_score(y_test, prediction)
print(classification_report(y_test, prediction)) | code |
122262213/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/iris-flower-dataset/IRIS.csv')
df.columns
df.head() | code |
122262213/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_text
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train) | code |
122262213/cell_7 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_text
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
prediction = clf.predict(X_test)
accuracy_score(y_test, prediction) | code |
122262213/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_text
import pandas as pd
df = pd.read_csv('/kaggle/input/iris-flower-dataset/IRIS.csv')
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
prediction = clf.predict(X_test)
accuracy_score(y_test, prediction)
pd.crosstab(y_test, prediction, rownames=['Reel'], colnames=['Predisction']) | code |
122262213/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/iris-flower-dataset/IRIS.csv')
df.columns | code |
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