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Machine Learning
House Price Prediction With Machine Learning
Predicting the sale prices of houses is an important task for real estate businesses and investors. In this article, we will explore various Multiple Linear Regression algorithms to predict continuous variables and provide insights into which algorithm is best suited for this task. Multiple Linear Regression is an algorithm that is commonly used in machine learning to predict values that are continuous in nature. It is a great algorithm to start with for beginners in ML. We will be using several linear regression algorithms, including the Ordinary Least Square (OLS) algorithm, Ridge regression algorithm. These algorithms can be implemented in Python using the scikit-learn package. To determine the best model for the given task, we will evaluate each algorithm using evaluation metrics provided by the scikit-learn package. By the end of this article, you will have a better understanding of Multiple Linear Regression algorithms and be able to apply them to predict sale prices.
To access the complete Python code and analysis in Jupyter IDE.
In this project, I aimed to help a real estate company optimize the sale prices of properties in the Hyderabad region by analyzing a dataset containing information about the prices of properties and their important features such as area, number of rooms, bathrooms, and parking.
To achieve this, I’ve used a statistical technique called Multiple Linear Regression, which allowed us to identify the variables affecting house prices and create a linear model that relates these variables with house prices in a quantitative manner.
I have used the data base given by the real estate company and filtered it in excel accordingly, which includes information about various properties in the Hyderabad region, to train our model and evaluated its accuracy to determine how well it can predict house prices based on the given variables.
Overall, my approach helped the real estate company to gain insights into the factors that affect property prices and create a more informed pricing strategy based on the features of a property.
Data Analytics
Concrete’s compressive strength for commercial buildings
Concrete compressive strength refers to the capacity of concrete to resist compressive stress, which is a vital property in civil engineering applications. This strength is measured in megapascals (MPa) and can vary widely depending on the composition and quality of the concrete mixture. Machine learning and hyperparameter tuning are used to predict the compressive strength of concrete. By training a machine learning model on a large dataset of concrete mixture compositions and their corresponding compressive strengths, the model can learn to predict the compressive strength of new concrete mixtures with a high degree of accuracy. Hyperparameter tuning is an important technique used in machine learning to optimize the performance of a model by adjusting its parameters. In the case of concrete compressive strength prediction, hyperparameter tuning can be used to fine-tune the machine learning model to achieve the highest possible accuracy. In summary, machine learning and hyperparameter tuning are used for concrete compressive strength prediction to improve the accuracy of strength predictions, which is essential for ensuring the safety and reliability of civil engineering structures.
To access the complete Python code and analysis in Jupyter IDE.
Problem Statement: Concrete is one of the most widely used building materials, consisting of three basic components: water, aggregate, and Portland cement. However, predicting the exact concrete compressive strength for residential complexes remains difficult due to the high complexity relationship between these components. This can lead to expensive raw materials and wastage of resources, which is not ideal for construction companies.
Solution: To address this problem, We have utilize advances in machine learning and data science to build a predictive model for concrete compressive strength. With this model, we have reduced the time it takes to determine whether the obtained CCS value will be suitable for building a residential complex. By accurately predicting the compressive strength, we have also reduced the cost of raw materials and minimize the wastage of resources.
Techniques Used: To build the predictive model for concrete compressive strength, we have used machine learning algorithms such as multiple linear regression, decision trees, or support vector regression. We have also employ hyperparameter tuning techniques to optimize the performance of our models. These techniques allowed us to fine-tune the model parameters to achieve the best possible accuracy in our predictions.
Why These Techniques are Used: Multiple linear regression can help us identify the significant factors that affect concrete compressive strength, such as the water-cement ratio, aggregate size, and curing time. Decision trees and support vector regression can also provide accurate predictions by analyzing complex relationships between these factors. Hyperparameter tuning techniques can optimize the performance of our models, ensuring that we get the most accurate predictions with the least amount of error.
In conclusion, machine learning and data science can provide powerful tools for predicting concrete compressive strength. By using these techniques, we have reduced the cost of raw materials and minimize wastage, leading to more efficient and sustainable construction practices.
Deep Learning
Stock Price Prediction using Deep Learning
Stock price prediction using deep learning is a popular application of artificial intelligence that can help traders and investors make informed decisions. Deep learning models, such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and convolutional neural networks (CNNs), are used to predict future stock prices based on historical data.
To access the complete Python code and analysis in Jupyter IDE.
Problem Statement: The stock market is a complex system, and predicting stock prices accurately is a challenging task. It requires the analysis of a vast amount of data from different sources, including historical stock prices, financial statements, and news articles. Accurate stock price prediction can help investors make informed investment decisions and increase their chances of earning higher returns. The problem statement, therefore, is to develop a deep learning model that can predict stock prices accurately.
Solution: In this project, I have used various deep learning techniques to predict stock prices. The techniques include a normal (cross-sectional) neural network, a Simplest recurrent neural network, a Simple RNN with more layers, LSTM (Long Short-Term Memory) neural network with one layer, LSTM (Long Short-Term Memory) neural network with more layers, GRU (Gated Recurrent Unit) neural network, and a 1D convolutional neural network.
To implement these techniques, I have used the Keras library, which is a high-level neural network API written in Python. Keras makes it easy to build deep learning models by providing a simple and intuitive interface.
We have used RNN (Recurrent Neural Network) models for stock price prediction because they are well-suited to handle sequential data. The LSTM and GRU models are particularly useful for capturing long-term dependencies in sequential data.
The 1D convolutional neural network is useful for capturing local patterns in the data. It works by applying filters to small segments of the data and then combining the results.
Conclusion: In this project, I have used various deep learning techniques to predict stock prices. I found that the Simple RNN with one layer and more layers models outperform the other techniques in terms of accuracy. These models are particularly useful for capturing long-term dependencies in sequential data. The 1D convolutional neural network also performed well and is useful for capturing local patterns in the data. Overall, this project demonstrates the effectiveness of deep learning techniques for predicting stock prices.
Data Visualization
As part of Data Visualization project, I gathered data to compare average sales price and cost price across different countries and makes. The data owners are considering withdrawing from a country or stopping the sale of a particular make if the cost price is too low compared to the sales price. I will also be analyzing labor costs by make and country, as the data owners are considering re-engineering their labor operations and outsourcing if the costs are too high. Finally, I will be looking at sales by color to determine the proportion of sales for each color and identify any colors with low sales that may be considered for discontinuation.
Below are the Charts
From this project, I have learned that the data owners are interested in analyzing several aspects of their business, including average sales price and cost price across different countries and makes, labor costs by make and country, and sales by color. They are considering withdrawing from a country or stopping the sale of a particular make if the cost price is too low compared to the sales price, or outsourcing labor operations if the costs are too high. Additionally, they may consider discontinuing the sale of colors with low sales proportions. As a data analyst, my role would be to gather and analyze data to provide insights on these various aspects of the business and help the data owners make informed decisions.
To gain deeper understanding of the project, please click on the button below for more insights.
Problem Statement: The task is to build a Qlik app and add the calculated fields of Gross Revenue, Cost, and Net Revenue. A dashboard needs to be created to answer specific questions such as the average Gross Revenue for each Country, average Cost, average Net Revenue, and average Gross Revenue for China and Japan, number of orders placed in each geographical area, proportion of Gross Revenue generated in each country, and total Net Revenue generated by each supplier in the United Kingdom. The dashboard needs to be exported to a PDF file, and a screenshot of the entire Qlik app needs to be taken.
Solution: A Qlik app was built and the calculated fields of Gross Revenue, Cost, and Net Revenue were added. A dashboard was created to answer specific questions such as the average Gross Revenue for each Country, average Cost, average Net Revenue, and average Gross Revenue for China and Japan, number of orders placed in each geographical area, proportion of Gross Revenue generated in each country, and total Net Revenue generated by each supplier in the United Kingdom. Finally, the dashboard was exported to a PDF file, and a screenshot of the entire Qlik app was taken.
Conclusion: The project was completed successfully with the use of a Qlik app and the addition of the calculated fields of Gross Revenue, Cost, and Net Revenue. The dashboard created provided answers to specific questions related to revenue and orders placed in various geographical areas.
Business Strategy
I have formulated a set of business strategies to promote growth in my previous company. These strategies are focused on various aspects of the business, such as marketing, sales, operations, and finance, and have been tailored to meet the specific needs and goals of the company. The objective of these strategies is to enhance the overall performance of the business by increasing revenue, reducing costs, improving customer experience, and increasing market share. The strategies may involve the implementation of new technologies, introduction of new products or services, or changes to existing business processes.
I have formulated a set of business strategies to promote growth in my previous company. These strategies are focused on various aspects of the business, such as marketing, sales, operations, and finance, and have been tailored to meet the specific needs and goals of the company. The objective of these strategies is to enhance the overall performance of the business by increasing revenue, reducing costs, improving customer experience, and increasing market share. The strategies may involve the implementation of new technologies, introduction of new products or services, or changes to existing business processes.
I have formulated a set of business strategies to promote growth in my previous company. These strategies are focused on various aspects of the business, such as marketing, sales, operations, and finance, and have been tailored to meet the specific needs and goals of the company. The objective of these strategies is to enhance the overall performance of the business by increasing revenue, reducing costs, improving customer experience, and increasing market share. The strategies may involve the implementation of new technologies, introduction of new products or services, or changes to existing business processes.
Data Migration for Collins Aerospace.
As a key member of the Collins Aerospace team, I played an integral role in the data migration process. My responsibilities included collaborating with cross-functional teams to understand data requirements, conducting data analysis to identify inconsistencies and discrepancies, and developing and executing data migration plans to ensure a seamless transition. I worked closely with stakeholders to ensure data integrity and accuracy and provided support to end-users to facilitate a smooth transition. My contributions to the data migration process enabled Collins Aerospace to modernize its data infrastructure, improve data accuracy and accessibility, and achieve its business objectives.
Data Migration for Collins Aerospace.
As a key member of the Collins Aerospace team, I played an integral role in the data migration process. My responsibilities included collaborating with cross-functional teams to understand data requirements, conducting data analysis to identify inconsistencies and discrepancies, and developing and executing data migration plans to ensure a seamless transition. I worked closely with stakeholders to ensure data integrity and accuracy and provided support to end-users to facilitate a smooth transition. My contributions to the data migration process enabled Collins Aerospace to modernize its data infrastructure, improve data accuracy and accessibility, and achieve its business objectives.
Data Migration for Collins Aerospace.
As a key member of the Collins Aerospace team, I played an integral role in the data migration process. My responsibilities included collaborating with cross-functional teams to understand data requirements, conducting data analysis to identify inconsistencies and discrepancies, and developing and executing data migration plans to ensure a seamless transition. I worked closely with stakeholders to ensure data integrity and accuracy and provided support to end-users to facilitate a smooth transition. My contributions to the data migration process enabled Collins Aerospace to modernize its data infrastructure, improve data accuracy and accessibility, and achieve its business objectives.
