![import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
path = os.getcwd() + 'dataex1data1.txt'
data = pd.read_csv(path, header=None, names=['Population', 'Profit'])
data.head()](https://image.slidesharecdn.com/statisticsindatasciencewithpython-180324222118/85/Statistics-in-Data-Science-with-Python-10-320.jpg)
![Implementing Simple Linear Regression
def computeCost(X, y, theta):
inner = np.power(((X * theta.T) - y), 2)
return np.sum(inner) / (2 * len(X)
# append a ones column to the front of the data set
data.insert(0, 'Ones', 1)
# set X (training data) and y (target variable)
cols = data.shape[1]
X = data.iloc[:,0:cols-1]
y = data.iloc[:,cols-1:cols]
# convert from data frames to numpy matrices
X = np.matrix(X.values)
y = np.matrix(y.values)
theta = np.matrix(np.array([0,0]))](https://image.slidesharecdn.com/statisticsindatasciencewithpython-180324222118/85/Statistics-in-Data-Science-with-Python-13-320.jpg)
![x = np.linspace(data.Population.min(), data.Population.max(), 100)
f = g[0, 0] + (g[0, 1] * x)
fig, ax = plt.subplots(figsize=(12,8))
ax.plot(x, f, 'r', label='Prediction')
ax.scatter(data.Population, data.Profit, label='Traning Data')
ax.legend(loc=2)
ax.set_xlabel('Population')
ax.set_ylabel('Profit')
ax.set_title('Predicted Profit vs. Population Size')](https://image.slidesharecdn.com/statisticsindatasciencewithpython-180324222118/85/Statistics-in-Data-Science-with-Python-14-320.jpg)
Statistics in Data Science with Python
- 1. Statistics in Data Science with Python
- 2. Mahe Karim Front End Developer ID - 162-15-7770 Area of Interest: Full Stack Developer Data Analyst Animation Why Not Jump Into Passive Income ? ;) Who I Am ?
- 4. Implement of our course Step 1 Step 2 Step 3 •Statistics •Data Science •Python
- 5. Basic RoadTo Data Science Statistics Machine Learning Deep Learning Programming Language ( Python / R ) Data Science
- 6. Smartest way to be a Data Scientist / Analyst • Core Statistics • Statistical Machine Learning • Probabilistic Modeling Step 1 Statistics • Database • Data Mining • Data Design Step 2 Computing • Deep Learning • NLP • DataAnalysis Step 3 ML
- 7. 3 steps to learning the statistics and probability required for data science: • Descriptive statistics, distributions, hypothesis testing, and regression. Core Statistics Concepts • Conditional probability, priors, posteriors, and maximum likelihood. BayesianThinking • Learn basic machine concepts and how statistics fits in. Intro to Statistical Machine Learning
- 8. Verified course include STATISTICS
- 9. Most ImportantTopics In Statistics • Part 1 - Simple Linear Regression Part 2 - Multivariate Linear Regression Part 3 - Logistic Regression Part 4 - Multivariate Logistic Regression Part 5 - Neural Networks Part 6 - SupportVector Machines Part 7 - K-Means Clustering & PCA Part 8 - Anomaly Detection & Recommendation
- 10. import os import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline path = os.getcwd() + 'dataex1data1.txt' data = pd.read_csv(path, header=None, names=['Population', 'Profit']) data.head()
- 11. Data Set:
- 13. Implementing Simple Linear Regression def computeCost(X, y, theta): inner = np.power(((X * theta.T) - y), 2) return np.sum(inner) / (2 * len(X) # append a ones column to the front of the data set data.insert(0, 'Ones', 1) # set X (training data) and y (target variable) cols = data.shape[1] X = data.iloc[:,0:cols-1] y = data.iloc[:,cols-1:cols] # convert from data frames to numpy matrices X = np.matrix(X.values) y = np.matrix(y.values) theta = np.matrix(np.array([0,0]))
- 14. x = np.linspace(data.Population.min(), data.Population.max(), 100) f = g[0, 0] + (g[0, 1] * x) fig, ax = plt.subplots(figsize=(12,8)) ax.plot(x, f, 'r', label='Prediction') ax.scatter(data.Population, data.Profit, label='Traning Data') ax.legend(loc=2) ax.set_xlabel('Population') ax.set_ylabel('Profit') ax.set_title('Predicted Profit vs. Population Size')
- 15. Prediction ;) :D :p <3
- 16. Resources: https://elitedatascience.com/learn-statistics-for-data-science https://github.com/datasciencemasters/go An Introduction to Statistical Learning with Applications in R Gareth James, DanielaWitten,Trevor Hastie and RobertTibshirani http://www.johnwittenauer.net/machine-learning-exercises-in- python-part-1/ Think Stats