ksv-muralidhar

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
from sklearn.preprocessing import MinMaxScaler

nifty = pd.read_csv('nifty.csv', usecols=['Date', 'Close'], parse_dates=True)

#nifty =  Nifty data from Jan 1, 2020 to 18 Sep, 2020
nifty = nifty.loc[(nifty['Date'] >= "2020-01-01") & (nifty['Date'] <= "2020-09-18")].copy()

ksv-muralidhar

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

data = pd.read_csv('netflix_titles.csv')

data = data.loc[data['release_year'].isin([*range(2016, 2020)]), ['type', 'release_year']].copy()
data.dropna(inplace=True)
data['release_year'] = data['release_year'].astype('int')
data

ksv-muralidhar

import pandas as pd
import matplotlib.pyplot as plt


fig, ax = plt.subplots(figsize=(12,5))
ax2 = ax.twinx()
ax.set_title('GDP per capita ($) and GDP growth rate')
ax.set_xlabel('Year')
ax.plot(gdp['date'], gdp[' GDP Per Capita (US $)'], color='green', marker='x')
ax2.plot(gdp['date'], gdp[' Annual Growth Rate (%)'], color='red', marker='o')

ksv-muralidhar

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.datasets import load_boston

X = load_boston()['data'].copy()
y = load_boston()['target'].copy()

linear_regression = LinearRegression()

ksv-muralidhar

<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
    package="com.example.mynews">

    <uses-permission android:name="android.permission.INTERNET" />

    <application
        android:allowBackup="true"
        android:icon="@mipmap/ic_launcher"
        android:label="@string/app_name"

ksv-muralidhar

import pandas as pd
from bs4 import BeautifulSoup
import requests as r
import streamlit as st

st.markdown('<h1 style="background-color: gainsboro; padding-left: 10px; padding-bottom: 20px;">Search Engine Scraper</h1>', unsafe_allow_html=True)
query = st.text_input('', help='Enter the search string and hit Enter/Return')
query = query.replace(" ", "+") #replacing the spaces in query result with +

if query: #Activates the code below on hitting Enter/Return in the search textbox

ksv-muralidhar

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

nifty = pd.read_csv("Nifty.csv",
                     usecols=['Date', "Close"],
                     parse_dates=['Date'])

nifty.set_index("Date", inplace=True) #setting "Date" as index

ksv-muralidhar

df.melt()

ksv-muralidhar

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import streamlit as st

st.title("Visualizing Central Limit Theorem")

POP_MIN, POP_MAX = st.sidebar.slider('Select the population range (Example: range of age is 0-100)',0, 10000, (0, 1000))

ksv-muralidhar

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.feature_selection import mutual_info_classif, SelectKBest
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import MinMaxScaler
import joblib