In the final part of this series, we deploy the regression model created in part 3 to a Streamlit web application. You will need a Streamlit account in order to do this. Check out the source code here.
Create a st_app.py file and place it in your main folder with the following code:
import numpy as np
import pandas as pd
import streamlit as st
#from sklearn.linear_model import lassost.set_page_config(
)st.title('Home Buyers Toolbox!')st.write('Use the sidebar to select a page to view.')page = st.sidebar.selectbox(
# def load_data():
This quick tutorial shows you how to get data from the AlphaVantage API, convert it into a handful of common technical indicators, and then plot the results.
Import all necessary libraries:
import pandas as pd
from io import StringIO
Get the data from AlphaVanatage API. You will need to sign up for a free API key here.
At the end of the second part of this series, we saved a copy of a cleaned and feature engineered data frame as a .csv file. This is the file that we want to use for building our regression models. The model file can be imported from Github if you fork and clone this repository.
Now the real fun begins and we start building models!
We are going to compare a few flavors of linear regression to see how they interact with the data. …
In part one of this series, we examine dealing with null and non-numeric values and begin exploratory data analysis (EDA) for the classic Ames Iowa Housing dataset. That article is available here.
As a reminder, the problem statement, the frame by which we are approaching the dataset, is that we are data scientists who are working for the fictitious company, Common Good Reality, based out of Ames, Iowa. We have been tasked to create a machine learning model to accurately predict house sale prices based on the provided Ames Iowa Housing dataset. In addition to creating a model that has…
The Ames Iowa housing dataset is a widely used dataset for teaching data science students fundamentals of regression analysis while providing good practice cleaning and exploring a dataset.
This post is a step by step walkthrough of preparing the Ames Iowa dataset for machine learning. At the end of this notebook, we will have prepared the dataset for feature engineering by removing all null values, verify data types, convert ordinal data into numeric types, and perform some preliminary EDA. …
In this post, I share an example Exploratory Data Analysis (EDA) project. The purpose of this project is to illustrate various techniques, visualizations, and practice use of Jupyter Notebooks and several Python libraries, the most notable of which being Pandas. If you are like me and very familiar with Excel, projects like this are invaluable in learning how to manipulate, clean, and examine data using Python. There is a substantial learning curve, but after a few weeks of practice the benefits and reproducibility of python will become very apparent and you will be remiss to use Excel again. This project…
I am a data scientist and analyst with a passion for mining golden insights from complex datasets.