1601193600

In this article I will continue with the Story of Logistic Regression, if you have not read the previous article, here is the **link**

Let me make things clear and simple, even in this article i won’t be able to complete Logistic Regression because its a broad topic and we are covering it in detail so it is better to plan what we gonna cover in this article.

*Probablistic approach of sigma function.**Optimization Theory.**We will derive optimization problem of LR by geometry.**WEIGHT VECTOR**We will deal with Overfitting problem.**We will see L1 and L2 regularization in detail.*

So lets get started,

#data-science #interview-questions #machine-learning #data-science-interview #logistic-regression

1598352300

Machine learning algorithms are not your regular algorithms that we may be used to because they are often described by a combination of some complex statistics and mathematics. Since it is very important to understand the background of any algorithm you want to implement, this could pose a challenge to people with a non-mathematical background as the maths can sap your motivation by slowing you down.

In this article, we would be discussing linear and logistic regression and some regression techniques assuming we all have heard or even learnt about the Linear model in Mathematics class at high school. Hopefully, at the end of the article, the concept would be clearer.

**Regression Analysis **is a statistical process for estimating the relationships between the **dependent variables ( say Y)** and one or more

#regression #machine-learning #beginner #logistic-regression #linear-regression #deep learning

1601193600

In this article I will continue with the Story of Logistic Regression, if you have not read the previous article, here is the **link**

Let me make things clear and simple, even in this article i won’t be able to complete Logistic Regression because its a broad topic and we are covering it in detail so it is better to plan what we gonna cover in this article.

*Probablistic approach of sigma function.**Optimization Theory.**We will derive optimization problem of LR by geometry.**WEIGHT VECTOR**We will deal with Overfitting problem.**We will see L1 and L2 regularization in detail.*

So lets get started,

#data-science #interview-questions #machine-learning #data-science-interview #logistic-regression

1600123860

Linear Regression and Logistic Regression are** two algorithms of machine learning **and these are mostly used in the data science field.

**Linear Regression**:> It is one of the algorithms of machine learning which is used as a technique to solve various use cases in the data science field. It is generally used in the case of **continuous output**. For e.g if ‘Area’ and ‘Bhk’ of the house is given as an input and we have found the ‘Price’ of the house, so this is called a regression problem.

Mechanism:> In the diagram below X is input and Y is output value.

#machine-learning #logistic-regression #artificial-intelligence #linear-regression

1597019820

In this article, I will be explaining how to use the concept of regression, in specific logistic regression to the problems involving classification. Classification problems are everywhere around us, the classic ones would include mail classification, weather classification, etc. All these data, if needed can be used to train a Logistic regression model to predict the class of any future example.

This article is going to cover the following sub-topics:

- Introduction to classification problems.
- Logistic regression and all its properties such as hypothesis, decision boundary, cost, cost function, gradient descent, and its necessary analysis.
- Developing a logistic regression model from scratch using python, pandas, matplotlib, and seaborn and training it on the Breast cancer dataset.
- Training an in-built Logistic regression model from sklearn using the Breast cancer dataset to verify the previous model.

Classification problems can be explained based on the Breast Cancer dataset where there are two types of tumors (Benign and Malignant). It can be represented as:

where

This is a classification problem with 2 classes, 0 & 1. Generally, the classification problems have multiple classes say, 0,1,2 and 3.

The link to the Breast cancer dataset used in this article is given below:

Predict whether the cancer is benign or malignant

- Let’s import the dataset to a pandas dataframe:

```
import pandas as pd
read_df = pd.read_csv('breast_cancer.csv')
df = read_df.copy()
```

2. The following dataframe is obtained:

```
df.head()
![Image for post](https://miro.medium.com/max/1750/1*PPyiGgocvjHbgIcs9yTWTA.png)
```

df.info()

Let us plot the mean area of the clump and its classification and see if we can find a relation between them.

```
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder()
df.diagnosis = label_encoder.fit_transform(df.diagnosis)
sns.set(style = 'whitegrid')
sns.lmplot(x = 'area_mean', y = 'diagnosis', data = df, height = 10, aspect = 1.5, y_jitter = 0.1)
```

We can infer from the plot that most of the tumors having an area less than 500 are benign(represented by zero) and those having area more than 1000 are malignant(represented by 1). The tumors having a mean area between 500 to 1000 are both benign and malignant, therefore show that the classification depends on more factors other than mean area. A linear regression line is also plotted for further analysis.

#machine-learning #logistic-regression #regression #data-sceince #classification #deep learning

1592023980

Take your current understanding and skills on machine learning algorithms to the next level with this article. What is regression analysis in simple words? How is it applied in practice for real-world problems? And what is the possible snippet of codes in Python you can use for implementation regression algorithms for various objectives? Let’s forget about boring learning stuff and talk about science and the way it works.

#linear-regression-python #linear-regression #multivariate-regression #regression #python-programming