aniruddha27

/* 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),

aniruddha27

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')

aniruddha27

# 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()

aniruddha27

# 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)

aniruddha27

# 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", 

aniruddha27

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

aniruddha27

import pandas as pd
import numpy as np
df = pd.read_csv(r'C:\Users\Dell\Desktop\train_big_mart.csv')
df.head()

aniruddha27

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))

aniruddha27

df=pd.read_csv(r'C:/Users/Dell/Desktop/salary.csv')
df.head()