ajaymuktha

import { useContext } from "react";
import { TodoContext } from "../context/TodoContext";
import { REMOVE_TODO } from "./../context/ActionTypes";

const TodoList = () => {
  const { todos, dispatch } = useContext(TodoContext);

  return (
    <div>
      <h2>Todo List</h2>

ajaymuktha

import { useReducer, useContext, useState } from "react";
import { v4 } from "uuid";

import { ADD_TODO } from "./../context/ActionTypes";
import { TodoContext } from "../context/TodoContext";

const TodoForm = () => {
  
  const [task, setTask] = useState("")

ajaymuktha

from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X,Y)

ajaymuktha

dataset = pd.read_csv('Salaries.csv')
X = dataset.iloc[:,1:2].values
Y = dataset.iloc[:,2].values 

ajaymuktha

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0])], remainder='passthrough')
X = np.array(ct.fit_transform(X))

ajaymuktha

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train[:, 3:] = sc.fit_transform(X_train[:, 3:])
X_test[:, 3:] = sc.transform(X_test[:, 3:])

ajaymuktha

#To delete rows
data = pd.read_csv("data.csv", index_col ="Name" )   
data.drop(["Mark","Wick", "Jonas"],inplace = True) 
#To delete columns
data.drop(["Name", "City"], axis = 1, inplace = True) 
                            

ajaymuktha

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train[:, 3:] = sc.fit_transform(X_train[:, 3:])
X_test[:, 3:] = sc.transform(X_test[:, 3:])

ajaymuktha

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(y)

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from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
imputer.fit(X[:, 1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3])
print(X)