kshirsagarsiddharth

X_train, X_test, y_train, y_test \
    = train_test_split(X, y, test_size=0.1, random_state=1)

X_train, X_valid, y_train, y_valid \
    = train_test_split(X_train, y_train, test_size=0.1, random_state=1)
X_train.shape, X_test.shape, X_valid.shape
((4131, 8), (511, 8), (459, 8))

kshirsagarsiddharth

from sqlalchemy import create_engine 
engine = create_engine("sqlite:///car_prediction_dataset.sqlite3")

query = """
SELECT
       year,
       price,
       km_traveled,
       tax,
       enginesize,

kshirsagarsiddharth

%%sql 
results << SELECT 
       year,
       price,
       km_traveled,
       tax,
       enginesize,
       km_per_liters,
       mi.model,
       transmission,

kshirsagarsiddharth

kshirsagarsiddharth

# find the distribution of website sessions in morning, afternoon, evening and night 
%%sql
result << 
SELECT 
    CASE
        WHEN HOUR(created_at) BETWEEN 7 AND 12 THEN 'morning'
        WHEN HOUR(created_at) BETWEEN 12 AND 16 THEN 'afternoon'
        WHEN HOUR(created_at) BETWEEN 16 AND 20 THEN 'evening'
        ELSE 'night'
    END AS divide,

kshirsagarsiddharth

# I have stored my credentials in a json file 
import json 
import pandas as pd 
import plotly.express as px
with open("creds.json","r") as f:
    creds = json.load(f)
# my username
user = creds['user']
# my password
password = creds['password']

kshirsagarsiddharth

kshirsagarsiddharth

A = interactive(scatter_plot_int,
             x = widgets.Dropdown(
                 options = ['person_age','person_income','person_emp_length','loan_amnt','loan_int_rate','loan_percent_income','cb_person_cred_hist_length']
                 ),
             y = widgets.Dropdown(
                 options = ['person_age','person_income','person_emp_length','loan_amnt','loan_int_rate','loan_percent_income','cb_person_cred_hist_length']
                 )
            )

B = interactive(scatter_plot_int_with_hue,

kshirsagarsiddharth

def interactive_crosstab(index = 'loan_grade', column = 'loan_intent'):
    crosstab = pd.crosstab(df[index], df[column]).style.text_gradient(cmap = 'icefire').applymap(lambda x : 'font-size:22.2px; font-weight:bold')
    return (crosstab.set_table_styles([
                    {
                        "selector":"thead",
                        "props": [("background-color", "#d0d0df"), 
                                  ("color", "black"),
                                  ("font-size", "20px"), ("font-style", "bold")]
                    },
                    {

kshirsagarsiddharth

def visualize_pivot_tables(index = 'person_home_ownership', column = 'loan_grade', values = 'loan_amnt', axis = 0):
    color_palette = sns.color_palette("vlag_r", as_cmap=True)
    t = pd.pivot_table(data = df, index = index, columns = column, values = values)
    return t.style.background_gradient(color_palette,axis = axis).applymap(lambda x : 'font-size:17.2px; font-weight:bold; opacity:0.9')
E = interact(visualize_pivot_tables,
             
             index = widgets.Dropdown(
                 options = ['person_home_ownership', 'loan_intent', 'loan_grade','cb_person_default_on_file','loan_status']
                 ),
             column = widgets.Dropdown(