peeush-agarwal

def multicollinearity_by_vif(X, vif=5):
    """Remove columns from X whose VIF is greater than supplied 'vif'
    Parameters:
        X:array or dataframe containing data excluding target variable
        vif: int or float of limiting value of VIF
    Note:
        This function changes X inplace
    """
    import statsmodels.api as sm
    from statsmodels.stats.outliers_influence import variance_inflation_factor

peeush-agarwal

import numpy as np

# data as pandas DataFrame
train = ...

# Variables to store out bounds by column value
upper_bounds = {}
lower_bounds = {}

# Best practice

peeush-agarwal

{
  "basics": {
    "name": "Peeush Agarwal",
    "label": "Senior ML Engineer",
    "email": "[email protected]",
    "phone": "+91 82377 28795",
    "location": {
      "city": "Pune",
      "region": "Maharashtra",
      "countryCode": "IN"

peeush-agarwal

{
    "basics": {
        "name": "Megha Goyal",
        "label": "WFM Business Partner | Chief of Staff | MIS Executive",
        "email": "[email protected]",
        "phone": "+91-90961 69255",
        "location": {
            "address": "Wagholi",
            "city": "Pune",
            "region": "Maharashtra"

peeush-agarwal

import numpy as np

cov_mat = np.cov(X_train_std.T)
eigen_vals, eigen_vecs = np.linalg.eig(cov_mat)

peeush-agarwal

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# split into training and testing sets
X, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3,
    stratify=y, random_state=0
)
# standardize the features

peeush-agarwal

import pandas as pd

df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/'
                      'machine-learning-databases/wine/wine.data',
                      header=None)
df_wine.head()

peeush-agarwal

# This is a basic workflow to help you get started with Actions

name: Deploy to Raspberry Pi

# Controls when the workflow will run
on:
  # Triggers the workflow on push or pull request events but only for the main branch
  push:
    branches: [ main ]

peeush-agarwal

Dockerfile
__pycache__/

peeush-agarwal

#!/bin/sh
gunicorn -w 1 -b 0.0.0.0:4000 app:app