mahimairaja

import torch

x = torch.ones(3) # Input Tensor
y = torch.zeros(2) # Expected Output Tensor

w = torch.randn(3, 2, requires_grad=True) # Weight matrix
b = torch.randn(2 , requires_grad=True) # Bias

z = torch.matmul(x, w) + b # Calculated output

mahimairaja

import pandas as pd
import json

def readJson(filename,) -> dict:
    """ 
    Reads a json file and returns a dictionary
    """
    file = open(filename, 'r')
    data = file.read()
    file.close()

mahimairaja

from super_gradients.training import models
import supervision as sv
import torch
import cv2

# install the required packages
# pip install -q super-gradients==3.1.1 supervision

# predict function to predict the image
def predict(img, model):

mahimairaja

# pip install rembg

from PIL import Image
from rembg import remove

input = Image.open('mahimai.jpeg')
output = remove(input)
output.save('output.png')

mahimairaja

from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv('your_csv_name')
col = df['Grade']
density = stats.gaussian_kde(col)

col.plot.density()

mahimairaja

# Author : Mahimai Raja J
# Date : 24.01.2023

from sklearn.preprocessing import StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

# Define preprocessing for numeric columns (scale them)
feature_columns = [0,1,2,3]

mahimairaja

# Train the model
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LinearRegression


# Define preprocessing for numeric columns (scale them)