accessnash’s gists

accessnash / portfolio_risk_ch1.py

Last active July 16, 2018 18:14

Intro to Portfolio Risk Management in Python - course on Datacamp - Chapter 1

	import pandas as pd
	import numpy as np
	from scipy.stats import skew
	from scipy.stats import kurtosis
	from scipy import stats
	from scipy.stats import shapiro

	# Chapter 1: Univariate Investment Risk and Returns

	StockPrices = pd.read_csv('StockData.csv', parse_dates=['Date'])

accessnash / portfolio_risk_ch2.py

Last active July 16, 2018 20:10

Intro to Portfolio Risk Management in Python - course on Datacamp - Chapter 2

	# Chapter 2 : Portfolio Investing

	portfolio_weights = np.array([0.12, 0.15, 0.08, 0.05, 0.09, 0.10, 0.11, 0.14, 0.16])

	# Calculate the weighted stock returns
	WeightedReturns = StockReturns.mul(portfolio_weights, axis=1)

	# Calculate the portfolio returns
	StockReturns['Portfolio'] = WeightedReturns.sum(axis=1)

accessnash / portfolio_risk_ch3.py

Created July 16, 2018 21:32

Intro to Portfolio Risk Management in Python - course on Datacamp - Chapter 3

	# Chapter 3 : Factor investing

	# Calculate excess portfolio returns
	FamaFrenchData['Portfolio_Excess'] = FamaFrenchData['Portfolio'] - FamaFrenchData['RF']

	# Plot returns vs excess returns
	CumulativeReturns = ((1+FamaFrenchData[['Portfolio','Portfolio_Excess']]).cumprod()-1)
	CumulativeReturns.plot()
	plt.show()

accessnash / portfolio_risk_ch4.py

Created July 17, 2018 13:26

Intro to Portfolio Risk Management in Python - course on Datacamp - Chapter 4

	# Chapter 4 : Value At Risk
	# Sub-topic : Estimating tail risk

	# Calculate the running maximum
	running_max = np.maximum.accumulate(cum_rets)

	# Ensure the value never drops below 1
	running_max[running_max < 1] = 1

	# Calculate the percentage drawdown

accessnash / iris_EDA.py

Last active July 18, 2018 18:29

exploratory data analysis in Python for the Iris dataset from UCI library

	# Import plotting modules
	import matplotlib.pyplot as plt
	import seaborn as sns
	# Import numpy
	import numpy as np

	# Set default Seaborn style
	sns.set()

accessnash / knn_voting.py

Last active July 19, 2018 18:19

fit a k-Nearest Neighbors classifier to the Congressional voting record dataset (scikit learn)- Datacamp

	# Supervised learning with scikit learn

	from sklearn import datasets
	import matplotlib.pyplot as plt

	# Import KNeighborsClassifier from sklearn.neighbors
	from sklearn.neighbors import KNeighborsClassifier

	# Create arrays for the features and the response variable
	y = df['party'].values

accessnash / gapminder_reg.py

Last active March 15, 2020 19:45

Predict the life expectancy in a given country using Gapminder data - OLS Regression, cross-validation, Lasso & Ridge regression (Supervised Learning with scikit learn at Datacamp)

	# Import numpy and pandas
	import numpy as np
	import pandas as pd

	# Read the CSV file into a DataFrame: df
	df = pd.read_csv('gapminder.csv')

	# Create arrays for features and target variable
	y = df['life']
	X = df['fertility']

accessnash / PIMA_Indians.py

Last active July 22, 2018 02:57

PIMA Indians dataset obtained from the UCI Machine Learning Repository - to predict whether or not a given female patient will contract diabetes based on features such as BMI, age, and number of pregnancies (Supervised Learning with scikit learn at Datacamp)

	# Import necessary modules
	from sklearn.model_selection import train_test_split
	from sklearn.metrics import classification_report
	from sklearn.metrics import confusion_matrix
	from sklearn.linear_model import LogisticRegression

	# Create training and test set
	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state=42)

	# Instantiate a k-NN classifier: knn

accessnash / gapminder_pipeline.py

Last active July 24, 2018 13:21

Preprocessing data using Gapminder data - Supervised Learning with scikit learn at Datacamp

	# Import pandas
	import pandas as pd

	# Read 'gapminder.csv' into a DataFrame: df
	df = pd.read_csv('gapminder.csv')

	# Create a boxplot of life expectancy per region
	df.boxplot('life', 'Region', rot=60)

	# Show the plot

accessnash / regression_seaborn.py

Created August 3, 2018 22:52

Visualizing regressions using Seaborn - Datacamp

	# Import plotting modules
	import matplotlib.pyplot as plt
	import seaborn as sns

	# Plot a linear regression between 'weight' and 'hp'
	sns.lmplot(x='weight', y='hp', data=auto)

	# Display the plot
	plt.show()