Fitting Logistic Regression import numpy as np import pandas as pd import statsmodels.api as sm df = pd.read_csv(‘./fraud_dataset.csv’) df.head() 1. As […]

## Simple Linear Regression

In this lesson, you will: Identify Regression Applications Learn How Regression Works Apply Regression to Problems Using Python Machine Learning is […]

## Case Study: A/B Tests

A/B tests are used to test changes on a web page by running an experiment where a control group sees the old […]

## Hypothesis Testing

rules for setting up null and alternative hypotheses: The H_0H0 is true before you collect any data. The H_0H0 usually states there is no […]

## Confidence Intervals – Udacity

import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline np.random.seed(42) full_data = pd.read_csv(‘../data/coffee_dataset.csv’) sample_data = […]

## Statistics – Udacity

Descriptive Statistics Descriptive statistics is about describing our collected data using the measures discussed throughout this lesson: measures of center, measures of […]

## Probability – Udacity

Probability Here you learned some fundamental rules of probability. Using notation, we could say that the outcome of a coin […]

## Project Notes – Udacity

b for new cell. x to delete cell.

m to markdown

## Data Analysis Process – Case Study 2 – Udacity

Drawing Conclusions

## Data Analysis Process – Case Study 1 – Udacity

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

% matplotlib inline