Introduction To Scikit Learn | Scikit-Learn | Python | California Housing Price Prediction
Video Link : https://www.youtube.com/watch?v=YoTWYlz2lp0&t=6s
Scikit-learn (formerly scikits.learn and also known as sklearn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. [Credits : Wikipedia]
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When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.
When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,
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Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
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This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (1:45) Installing Python & PyCharm
⌨️ (6:40) Setup & Hello World
⌨️ (10:23) Drawing a Shape
⌨️ (15:06) Variables & Data Types
⌨️ (27:03) Working With Strings
⌨️ (38:18) Working With Numbers
⌨️ (48:26) Getting Input From Users
⌨️ (52:37) Building a Basic Calculator
⌨️ (58:27) Mad Libs Game
⌨️ (1:03:10) Lists
⌨️ (1:10:44) List Functions
⌨️ (1:18:57) Tuples
⌨️ (1:24:15) Functions
⌨️ (1:34:11) Return Statement
⌨️ (1:40:06) If Statements
⌨️ (1:54:07) If Statements & Comparisons
⌨️ (2:00:37) Building a better Calculator
⌨️ (2:07:17) Dictionaries
⌨️ (2:14:13) While Loop
⌨️ (2:20:21) Building a Guessing Game
⌨️ (2:32:44) For Loops
⌨️ (2:41:20) Exponent Function
⌨️ (2:47:13) 2D Lists & Nested Loops
⌨️ (2:52:41) Building a Translator
⌨️ (3:00:18) Comments
⌨️ (3:04:17) Try / Except
⌨️ (3:12:41) Reading Files
⌨️ (3:21:26) Writing to Files
⌨️ (3:28:13) Modules & Pip
⌨️ (3:43:56) Classes & Objects
⌨️ (3:57:37) Building a Multiple Choice Quiz
⌨️ (4:08:28) Object Functions
⌨️ (4:12:37) Inheritance
⌨️ (4:20:43) Python Interpreter
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=rfscVS0vtbw&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3
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In this tutorial, we’re going to create a model to predict House prices🏡 based on various factors across different markets.
The goal of this statistical analysis is to help us understand the relationship between house features and how these variables are used to predict house price.
Data used: Kaggle-kc_house Dataset
GitHub: you can find my source code here
First, Let’s import the data and have a look to see what kind of data we are dealing with:
#import required libraries import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt #import Data Data = pd.read_csv('kc_house_data.csv') Data.head(5).T #get some information about our Data-Set Data.info() Data.describe().transpose()
5 records of our dataset
Information about the dataset, what kind of data types are your variables
Statistical summary of your dataset
Our features are:
✔️**Date:**_ Date house was sold_
✔️**Price:**_ Price is prediction target_
✔️**_Bedrooms: _**Number of Bedrooms/House
✔️**Bathrooms:**_ Number of bathrooms/House_
✔️**Sqft_Living:**_ square footage of the home_
✔️**Sqft_Lot:**_ square footage of the lot_
✔️**Floors:**_ Total floors (levels) in house_
✔️**Waterfront:**_ House which has a view to a waterfront_
✔️**View:**_ Has been viewed_
✔️**Condition:**_ How good the condition is ( Overall )_
✔️**Grade:**_ grade given to the housing unit, based on King County grading system_
✔️**Sqft_Above:**_ square footage of house apart from basement_
✔️**Sqft_Basement:**_ square footage of the basement_
✔️**Yr_Built:**_ Built Year_
✔️**Yr_Renovated:**_ Year when house was renovated_
✔️**Lat:**_ Latitude coordinate_
✔️**_Long: _**Longitude coordinate
✔️**Sqft_Living15:**_ Living room area in 2015(implies — some renovations)_
✔️**Sqft_Lot15:**_ lotSize area in 2015(implies — some renovations)_
Let’s plot couple of features to get a better feel of the data
#visualizing house prices fig = plt.figure(figsize=(10,7)) fig.add_subplot(2,1,1) sns.distplot(Data['price']) fig.add_subplot(2,1,2) sns.boxplot(Data['price']) plt.tight_layout() #visualizing square footage of (home,lot,above and basement) fig = plt.figure(figsize=(16,5)) fig.add_subplot(2,2,1) sns.scatterplot(Data['sqft_above'], Data['price']) fig.add_subplot(2,2,2) sns.scatterplot(Data['sqft_lot'],Data['price']) fig.add_subplot(2,2,3) sns.scatterplot(Data['sqft_living'],Data['price']) fig.add_subplot(2,2,4) sns.scatterplot(Data['sqft_basement'],Data['price']) #visualizing bedrooms,bathrooms,floors,grade fig = plt.figure(figsize=(15,7)) fig.add_subplot(2,2,1) sns.countplot(Data['bedrooms']) fig.add_subplot(2,2,2) sns.countplot(Data['floors']) fig.add_subplot(2,2,3) sns.countplot(Data['bathrooms']) fig.add_subplot(2,2,4) sns.countplot(Data['grade']) plt.tight_layout()
With distribution plot of price, we can visualize that most of the prices are between 0 and around 1M with few outliers close to 8 million (fancy houses😉). It would make sense to drop those outliers in our analysis.
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