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/* Sample data */ | |
insert into emp (EMPID, NAME, JOB, SALARY) | |
values | |
(201, 'ANIRUDDHA', 'ANALYST', 2100), | |
(212, 'LAKSHAY', 'DATA ENGINEER', 2700), | |
(209, 'SIDDHARTH', 'DATA ENGINEER', 3000), | |
(232, 'ABHIRAJ', 'DATA SCIENTIST', 2500), | |
(205, 'RAM', 'ANALYST', 2500), | |
(222, 'PRANAV', 'MANAGER', 4500), | |
(202, 'SUNIL', 'MANAGER', 4800), |
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import pandas as pd | |
import os | |
import shutil | |
from sklearn.model_selection import train_test_split | |
# Home directory | |
home_path = r'C:/Users/Dell/Desktop/Analytics Vidhya/ImageDataGenerator/emergency_vs_non-emergency_dataset/emergency_vs_non-emergency_dataset' | |
# Create train and validation directories | |
train_path = os.path.join(home_path,'train') |
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# text detection | |
def contours_text(orig, img, contours): | |
for cnt in contours: | |
x, y, w, h = cv2.boundingRect(cnt) | |
# Drawing a rectangle on copied image | |
rect = cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 255, 255), 2) | |
cv2.imshow('cnt',rect) | |
cv2.waitKey() |
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# multi-class classification | |
from sklearn.multiclass import OneVsRestClassifier | |
from sklearn.linear_model import LogisticRegression | |
from sklearn.model_selection import train_test_split | |
from sklearn.metrics import roc_curve | |
from sklearn.metrics import roc_auc_score | |
# generate 2 class dataset | |
X, y = make_classification(n_samples=1000, n_classes=3, n_features=20, n_informative=3, random_state=42) |
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# ImageDataGenerator flow_from_dataframe | |
df_train = pd.read_csv(home_path + r'/emergency_train.csv') | |
df_train['emergency_or_not'] = df_train['emergency_or_not'].astype('str') # requires target in string format | |
train_generator_df = datagen.flow_from_dataframe(dataframe=df_train, | |
directory=home_path+'/images/', | |
x_col="image_names", | |
y_col="emergency_or_not", | |
class_mode="binary", |
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df_apply = df.groupby(['Outlet_Establishment_Year'])['Item_MRP'].apply(lambda x: x - x.mean()) | |
df_apply |
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def filter_func(x): | |
return x['Item_Weight'].std() < 3 | |
df_filter = df.groupby(['Item_Weight']).filter(filter_func) | |
df_filter.shape |
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import pandas as pd | |
import numpy as np | |
df = pd.read_csv(r'C:\Users\Dell\Desktop\train_big_mart.csv') | |
df.head() |
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doc = nlp(' Last year, I spoke about the Ujjwala programme , through which, I am happy to report, 50 million free liquid-gas connections have been provided so far') | |
png = visualise_spacy_tree.create_png(doc) | |
display(Image(png)) |
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df=pd.read_csv(r'C:/Users/Dell/Desktop/salary.csv') | |
df.head() |
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