sakusuma

advanced-prompt-engineering-techniques-3817061

This repo is for LinkedIn Learning course: Advanced Prompt Engineering Techniques

AI-102-AIEngineer

Lab files for AI-102 - AI Engineer

AirlinePassengerPrediction

Need to predict how many passengers are going to opt for the airline base on the historical information provided by the Airlines. Using various Time series techniques predicted the number of passengers

CarPricePrediction

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts. They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know: Which variables are significant in predicting the price of a car How well those variables describe the price of a car Based on various market surveys, the consulting firm has gathered a large dataset of different types of cars across the Americal market.

CreditCardFraudDetection

Although digital transactions in India registered a 51% growth in 2018-19, their safety remains a concern. Fraudulent activities have increased severalfold, with around 52,304 cases of credit/debit card fraud reported in FY'19 alone. Due to this steep increase in banking frauds, it is the need of the hour to detect these fraudulent transactions in time in order to help consumers as well as banks, who are losing their credit worth each day. Machine learning can play a vital role in detecting fraudulent transactions. Imagine you get a call from your bank, and the customer care executive informs you that your card is about to expire in a week. Immediately, you check your card details and realise that it will expire in the next 8 days. Now, in order to renew your membership, the executive asks you to verify a few details such as your credit card number, the expiry date and the CVV number. Will you share these details with the executive? In such situations, you need to be careful because the details that you might share with them could grant them unhindered access to your credit card account.The aim of this project is to predict fraudulent credit card transactions using machine learning models. The data set that you will be working on during this project was obtained from Kaggle. It contains thousands of individual transactions that took place over a course of two days and their respective labels.

FnLTweetAnalysis

Supply chain as a industry saw 6X more disruptions in 2021 than any of the previous years.  Deloitte says 56 % of the companies are already using external data to understand how the external factors are influencing the industry they are in and its also said that the companies are planning to increase investments in getting and analyzing the external data This project is one such attempt to enable Supply Chain team of Microsoft Devices with the latest happenings around the world that could disrupt the flow of operations and cause delays in our supply chain using external data. Whenever a user has posted on public social media like twitter.com using any of the general keywords which we track, the tweet would be scraped immediately with in 5 mins. Once the tweet is fed into the data pool, the tweet gets analyzed for the language, sentiment, key phrases, translate if not in English, and finally gets relayed into a real time Power BI report which is readily available for the Supply chain team   

FundPrioritzation

HELP International is an international humanitarian NGO that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during the time of disasters and natural calamities. It runs a lot of operational projects from time to time along with advocacy drives to raise awareness as well as for funding purposes. After the recent funding programmes, they have been able to raise around $ 10 million. Now the CEO of the NGO needs to decide how to use this money strategically and effectively. The significant issues that come while making this decision are mostly related to choosing the countries that are in the direst need of aid. And this is where you come in as a data analyst. Your job is to categorise the countries using some socio-economic and health factors that determine the overall development of the country. Then you need to suggest the countries which the CEO needs to focus on the most. The datasets containing those socio-economic factors and the corresponding data dictionary are provided below.

NeuralNetworksandDeepLearning

A introduction to neural networks and the process that goes behind them using a image classification problem

SalesLeadScoring

An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses. The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals. Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%. Now, although X Education gets a lot of leads, its lead conversion rate is very poor. For example, if, say, they acquire 100 leads in a day, only about 30 of them are converted. To make this process more efficient, the company wishes to identify the most potential leads, also known as ‘Hot Leads’. If they successfully identify this set of leads, the lead conversion rate should go up as the sales team will now be focusing more on communicating with the potential leads rather than making calls to everyone.

TelecomChurn

The project is based on Indian and Southeast Asian market where mostly prepaid payment model is prevelant In this project we will use the usage-based chrun definition i.e. customers who have not done any usage either incoming or outgoing in terms of calls, internet etc. over a period of time. We focus only the High Value customers, as typically 80% of the revenue comes from top 20% of the customers The dataset spans data of four consecutive months between June - September. The objective is to predict the churn in the last month using the data from the first three months. There are typically three phases of a customer lifecycle - (a) Good Phase where the customer is happy with services. We have assumed month 6 and 7 as Good Phase period here.(b) Action Phase where customer experience starts to sore. We have assumed the 3rd month i.e. month 8 here for this (c) Churn Phase where the customer is said to have churned. This is equivalent to the month 9 here.

sakusuma/CreditCardFraudDetection

Although digital transactions in India registered a 51% growth in 2018-19, their safety remains a concern. Fraudulent activities have increased severalfold, with around 52,304 cases of credit/debit card fraud reported in FY'19 alone. Due to this steep increase in banking frauds, it is the need of the hour to detect these fraudulent transactions in time in order to help consumers as well as banks, who are losing their credit worth each day. Machine learning can play a vital role in detecting fraudulent transactions. Imagine you get a call from your bank, and the customer care executive informs you that your card is about to expire in a week. Immediately, you check your card details and realise that it will expire in the next 8 days. Now, in order to renew your membership, the executive asks you to verify a few details such as your credit card number, the expiry date and the CVV number. Will you share these details with the executive? In such situations, you need to be careful because the details that you might share with them could grant them unhindered access to your credit card account.The aim of this project is to predict fraudulent credit card transactions using machine learning models. The data set that you will be working on during this project was obtained from Kaggle. It contains thousands of individual transactions that took place over a course of two days and their respective labels.

sakusuma/AirlinePassengerPrediction

Need to predict how many passengers are going to opt for the airline base on the historical information provided by the Airlines. Using various Time series techniques predicted the number of passengers

sakusuma/CarPricePrediction

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts. They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know: Which variables are significant in predicting the price of a car How well those variables describe the price of a car Based on various market surveys, the consulting firm has gathered a large dataset of different types of cars across the Americal market.

sakusuma/FnLTweetAnalysis

Supply chain as a industry saw 6X more disruptions in 2021 than any of the previous years.  Deloitte says 56 % of the companies are already using external data to understand how the external factors are influencing the industry they are in and its also said that the companies are planning to increase investments in getting and analyzing the external data This project is one such attempt to enable Supply Chain team of Microsoft Devices with the latest happenings around the world that could disrupt the flow of operations and cause delays in our supply chain using external data. Whenever a user has posted on public social media like twitter.com using any of the general keywords which we track, the tweet would be scraped immediately with in 5 mins. Once the tweet is fed into the data pool, the tweet gets analyzed for the language, sentiment, key phrases, translate if not in English, and finally gets relayed into a real time Power BI report which is readily available for the Supply chain team   

sakusuma/advanced-prompt-engineering-techniques-3817061

This repo is for LinkedIn Learning course: Advanced Prompt Engineering Techniques

sakusuma/AI-102-AIEngineer

Lab files for AI-102 - AI Engineer

sakusuma/ailab

Experience, Learn and Code the latest breakthrough innovations with Microsoft AI

sakusuma/artificial-nose

Instructions, source code, and misc. resources needed for building a Tiny ML-powered artificial nose.

sakusuma/FundPrioritzation

HELP International is an international humanitarian NGO that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during the time of disasters and natural calamities. It runs a lot of operational projects from time to time along with advocacy drives to raise awareness as well as for funding purposes. After the recent funding programmes, they have been able to raise around $ 10 million. Now the CEO of the NGO needs to decide how to use this money strategically and effectively. The significant issues that come while making this decision are mostly related to choosing the countries that are in the direst need of aid. And this is where you come in as a data analyst. Your job is to categorise the countries using some socio-economic and health factors that determine the overall development of the country. Then you need to suggest the countries which the CEO needs to focus on the most. The datasets containing those socio-economic factors and the corresponding data dictionary are provided below.

sakusuma/NeuralNetworksandDeepLearning

A introduction to neural networks and the process that goes behind them using a image classification problem

sakusuma/SalesLeadScoring

An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses. The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals. Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%. Now, although X Education gets a lot of leads, its lead conversion rate is very poor. For example, if, say, they acquire 100 leads in a day, only about 30 of them are converted. To make this process more efficient, the company wishes to identify the most potential leads, also known as ‘Hot Leads’. If they successfully identify this set of leads, the lead conversion rate should go up as the sales team will now be focusing more on communicating with the potential leads rather than making calls to everyone.

sakusuma/TelecomChurn

The project is based on Indian and Southeast Asian market where mostly prepaid payment model is prevelant In this project we will use the usage-based chrun definition i.e. customers who have not done any usage either incoming or outgoing in terms of calls, internet etc. over a period of time. We focus only the High Value customers, as typically 80% of the revenue comes from top 20% of the customers The dataset spans data of four consecutive months between June - September. The objective is to predict the churn in the last month using the data from the first three months. There are typically three phases of a customer lifecycle - (a) Good Phase where the customer is happy with services. We have assumed month 6 and 7 as Good Phase period here.(b) Action Phase where customer experience starts to sore. We have assumed the 3rd month i.e. month 8 here for this (c) Churn Phase where the customer is said to have churned. This is equivalent to the month 9 here.

sakusuma/Azure-Computer-Vision-in-a-day-workshop

Azure Computer Vision workshop in a day

sakusuma/best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

sakusuma/CarPrice_R

Basic Linear Regression model in R language

sakusuma/DogShoesPricing

Simple linear regression,exporting model and using the same to alert users

sakusuma/DogsTemparaturePrediction

This notebook helps us predict the temperature of the dogs using simple regression, multiple regression and polynominal regression

sakusuma/DOLLinearRegressionInsideOut

Case study covered during Microsoft Devices Day of Learning : Linear Regression Inside - Out session

sakusuma/EDA_py

This notebook is a lab from the Microsoft learning courses.

sakusuma/feature-engineering-for-machine-learning

Code repository for the online course Feature Engineering for Machine Learning

sakusuma/HousePricePrediction

A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price. For the same purpose, the company has collected a data set from the sale of houses in Australia. The data is provided in the CSV file below. The company is looking at prospective properties to buy to enter the market. You are required to build a regression model using regularisation in order to predict the actual value of the prospective properties and decide whether to invest in them or not. The company wants to know: Which variables are significant in predicting the price of a house, and How well those variables describe the price of a house.

sakusuma/Mastering-GitHub-Copilot-for-Paired-Programming

A 6 Lesson course teaching everything you need to know about harnessing GitHub Copilot and an AI Paired Programing resource.

sakusuma/ML-For-Beginners

12 weeks, 24 lessons, classic Machine Learning for all

sakusuma/multivariate-lstm

sakusuma/natbot

Drive a browser with GPT-3

sakusuma/openai-cookbook

Examples and guides for using the OpenAI API

sakusuma/python-training

Python training for business analysts and traders

sakusuma/sakusuma

sakusuma/StockMarketAnalysis

Moving average use the past data to smoothen the price curve. For the purpose of this assignment, we will be using 20 Day and 50 Day moving averages. Now that you know about the concept of Moving average, you shall be wondering how to use it to determine whether to buy or sell a stock. When the shorter-term moving average crosses above the longer-term moving average, it is a signal to BUY, as it indicates that the trend is shifting up. This is known as a Golden Cross. On the opposite when the shorter term moving average crosses below the longer term moving average, it is a signal to SELL, as it indicates the trend is shifting down. It is sometimes referred to as the Death Cross. Please note that it is important that the Moving Averages Cross each other in order to generate a signal. Merely being above or below is not sufficient to generate a signal. When the signal is neither buy nor sell, it is classified as hold. If you already own the stock, keep it and if you don't then don't buy it now.

sakusuma/ToyMfgOptimization

A toy manufacturing organization manufactures two types of toys A and B. What should be the manufacturing quantity for each of the toys to maximize the profits?