Python Programming in Artificial Intelligence


What is Artificial Intelligence?

Artificial intelligence (AI) is the capacity of an advanced PC or PC controlled robot to perform undertakings regularly connected with humans. The term is now and again applied to the undertaking of creating frameworks blessed with the scholarly procedures normal for people, for example, the capacity to reason, find significance, sum up, or gain from past understanding. Since the improvement of the computerized PC during the 1940s, it has been shown that PCs can be modified to complete complex errands—as, finding proofs for scientific hypotheses or playing chess—with extraordinary capability. All things considered, regardless of proceeding with propels in PC preparing velocity and memory limit, there are so far no projects that can coordinate human adaptability over more extensive areas or in assignments requiring a lot of regular information. Then again, a few projects have accomplished the exhibition levels of human specialists and experts in playing out certain particular errands, so computerized reasoning in this constrained sense is found in applications as differing as a medicinal conclusion, PC web search tools, and voice and handwriting recognition.

Benefits of Python Programming in AI

The motivation behind why Python is the ideal programming language for AI and ML is because of the accompanying points of interest:

• A low passage boundary:

Python programming language looks like the regular English language, and that makes the way toward learning simpler. Its straightforward sentence structure enables you to easily work with complex frameworks, guaranteeing clear relations between the framework components.

  • Flexibility:

    Adaptability enables engineers to pick the programming styles which they are completely alright with or even join these styles to take care of various kinds of issues in the most productive manner.

• Platform freedom:

Python isn’t just agreeable to utilize and simple to adapt yet besides exceptionally flexible. Python for AI improvement can run on any stage including Windows, macOS, Linux, Unix, and twenty-one others. This sets aside time and cash for tests on different stages and makes the general procedure less complex and increasingly helpful.

• Readability:

Python is anything but difficult to peruse. Each Python engineer can get, change, duplicate or offer the code of their friends. There’s no perplexity, mistakes, or clashing standards, prompting a progressively productive trade of calculations, thoughts, and devices among AI and ML experts.

• Good perception alternatives:

In Artificial Intelligence and Machine Learning, it’s essential to speak to information in a comprehensible organization. Libraries like Matplotlib enable information researchers to fabricate outlines, histograms, and plots for better information appreciation, compelling introduction, and representation.

• Community support:

It’s in every case extremely accommodating when there’s a solid network bolster worked around the programming language. Python is an open-source language that implies that there’s a lot of assets open for software engineers beginning from tenderfoots and completion with geniuses. Python programming language is free with an assortment of valuable libraries and devices.

• Growing fame:

Python is turning out to be increasingly more well known among information researchers. The prominence of Python is anticipated to develop until 2020, in any event.

Machine Learning with Python

We will execute Machine Learning by utilizing Python.

Issue Statement: To fabricate a Machine Learning model that will foresee whether it will rain tomorrow by contemplating past information.

Dataset: This informational collection contains around 145k perceptions on the day by day climate conditions as saw from various Australian climate stations. The informational collection has around 24 features and we will utilize 23 features (Predictor factors) to foresee the target variable, which is, “Rain Tomorrow”.

This objective variable (Rain Tomorrow) will store two qualities:

1. Yes: Denotes that it will rain tomorrow

2. No: Denotes it won’t rain tomorrow

Hence, this is unmistakably a classification issue. The Machine Learning model will characterize the yield into 2 classes, either YES or NO.

Rationale: To assemble Classification models to foresee whether it will rain tomorrow dependent on the climate conditions.

Since the goal is clear, we should get our minds working and start coding.

Step 1: Import the required libraries

Step 2: Load the data set 

Step 3: Data Pre-processing:

Here, the variables that are not important are removed. Unnecessary variables will just increase our computation. Here to avoid this the variables “location” and “RISK_MM”(Amount of rain the next day) are removed. The best effort is made to avoid null values in the dataset. After the null values are removed the outliers need to avoid. After these the data is normalized.

Step 4: Exploratory Data Analysis (EDA)

Here we perform analysis and identify the significant variables that will help us predict the outcome.

‘X’ and ‘y’ denote the input and the output respectively.

Step 5: Building a Machine Learning Model

There are several machine learning models which are good at classification problem. We’ll look at Logistic Regression.

Deep Learning With Python

Problem: To study a bank debit data set and determine whether a transaction is fraudulent or not based on past data.

Data Set: The data set describes the transactions made by the Indian cardholders in the years 2018. It contains transactional details of three days, where there are 492 fraudulent activities out of 284,807 transactions.

Strategy: To build a Neural Network that can classify a transaction as either fraudulent or not based on past transactions.

Step 1: Import the necessary packages

Step 2: Load the data set

The target variable is the ‘Class’ variable.

1.Class 0: The transaction is not fraudulent

2.Class 1: The transaction is fraudulent

The rest of the variables are predictor variables that help us in predicting the transactions to be fraudulent or not.

A Probability sampling technique called Stratified Sampling is used to sample the data to avoid the high imbalance between the two classes(fraudulent and non-fraudulent transactions).

Step 3: Data Preparation

Here outliers are removed. Here time column is an outlier.

Step 4: Data Splicing

Here the dataset is split into training and test dataset.

Step 5: Data Normalisation

Step 6: Building the Neural Network

We will deploy a neural network containing 3 fully-connected layers. The first and second layer has 200 nodes. We will go with ReLu as an activation function. We will have an output layer with a single node. We will go with the Keras package. Adding Dropout Layers to avoid overfitting in a neural network.

Optimization is done on parameters(bias, weights) with Adam optimizer.

Step 7: Model Evaluation


Python offers many features that are helpful for AI and ML in particular, and that makes it the best language for these purposes. No wonder at various industries using Python for predictions and other machine learning tasks. A few of the Application fields are Travel, Transportation, and Healthcare.

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