samyumobi

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline
from sklearn.metrics import r2_score

samyumobi

samyumobi

from airflow.models import DAG
from airflow.contrib.sensors.file_sensor import FileSensor
from airflow.operators.bash_operator import BashOperator
from airflow.operators.python_operator import PythonOperator
from airflow.operators.python_operator import BranchPythonOperator
from airflow.operators.dummy_operator import DummyOperator
from airflow.operators.email_operator import EmailOperator
from dags.process import process_data
from datetime import datetime, timedelta

samyumobi

samyumobi

samyumobi

from google.colab.patches import cv2_imshow

## load new image and convert to gray scale
f2 = sys.argv[1]
img2 = cv2.imread('/iPhone_camera_1615895489376.webp',cv2.IMREAD_GRAYSCALE)
## ht, width of image
h, w = img2.shape
## horizontal edge detection
sobel_h = cv2.Sobel(img2, cv2.CV_64F, 1, 0, ksize = 5)
## Vertical edge detection

samyumobi

## Load the image
f3 = sys.argv[1]
img3 = img
## convert image to grayscale and display img
img3_gray = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)
cv2_imshow(img3_gray)
## histogram equalization of color images 
img3c = cv2.cvtColor(img3,cv2.COLOR_BGR2YUV)
## equalize the pixels across y channel and display
img3c[:,:,0] = cv2.equalizeHist(img3c[:,:,0])

samyumobi

import sys, cv2
import matplotlib.pyplot as plt

## Load the image
f = sys.argv[1]
img = cv2.imread('/a.jpeg')

## display the image
plt.imshow(img)
plt.show()

samyumobi

# Scaling the data
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

po = [('scaler',StandardScaler()),('logistic_reg',LogisticRegression(C=10))]
p = Pipeline(po)
lr4 = p.fit(X_train, Y_train)
print("Scaled Accuracy score : ",100*lr4.score(X_test,Y_test),"% \n")

# Logistic Regression coefficients

samyumobi

### Hyper parameter tuning Logistic Regression model

from sklearn.model_selection import GridSearchCV
from sklearn import linear_model
from sklearn.pipeline import make_pipeline

lr = linear_model.LogisticRegression(solver ='lbfgs', penalty = 'l2')

# Use gridsearch CV to search for the bes parameter