Naiive Bayes in scikit-learn
Naïve Bayes is a simple but powerful classifier based on a probabilistic model derived from the Bayes' theorem. Basically it determines the probability that an instance belongs to a class…
Decision Trees in scikit-learn
Decision trees are very simple yet powerful supervised learning methods, which constructs a decision tree model, which will be used to make predictions. The main advantage of this model is…
Support Vector Machine in scikit-learn
Support Vector Machines has become one of the state-of-the-art machine learning models for many tasks with excellent results in many practical applications. One of the greatest advantages of Support Vector…
Regression in scikit-learn
We will compare several regression methods by using the same dataset. We will try to predict the price of a house as a function of its attributes. In [6]: import numpy…
k-Nearest Neighbors(kNN)
k-Nearest Neighbors(kNN) Pros: High accuracy, insensitive to outliers, no assumptions about data Cons: Computationally expensive, requires a lot of memory Works with: Numeric values, nominal values We have an existing…
Predictive Analysis, Binary Classification (Cookbook)
This notebook contains my notes for Predictive Analysis on Binary Classification. It acts as a cookbook. Importing and sizing up a New Data Set The file is comma delimited, with…
Facebook Data Analysis
In [20]: import pandas as pd import numpy as np In [ ]: # Take few samples for the visualization sample_fbcheckin_train_tbl = fbcheckin_train_tbl[:10000].copy() In [21]: df = pd.read_csv('train.csv', index_col='row_id') In [22]: df.head() Out[22]: x y…
Linear Classification method with ScikitLearn
This blog is from the book and aimed to be as a learning material for myself only.Linear Classification method implements regularized linear models with stochastic gradient descent (SGD) learning. Each sample estimates…