[ FreeCourseWeb ] Udemy - House Price Prediction using Linear Regression and Python

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[ FreeCourseWeb ] Udemy - House Price Prediction using Linear Regression and Python (Size: 1.3 GB)
  1. Conclusion-en_US.srt 9.6 KB
  1. Conclusion.mp4 44.7 MB
  1. Dataset Explanation-en_US.srt 16 KB
  1. Dataset Explanation.mp4 66.4 MB
  1. Feature Engineering-en_US.srt 10.4 KB
  1. Feature Engineering.mp4 88.1 MB
  1. Import Packages-en_US.srt 8.4 KB
  1. Import Packages.mp4 47.2 MB
  1. Introduction of Projects-en_US.srt 15.3 KB
  1. Introduction of Projects.mp4 68.7 MB
  1. Predicting Result-en_US.srt 11.2 KB
  1. Predicting Result.mp4 78.7 MB
  2. Calculating Variance Inflation Factor-en_US.srt 15.2 KB
  2. Calculating Variance Inflation Factor.mp4 83.8 MB
  2. Data Preprocessing-en_US.srt 8.6 KB
  2. Data Preprocessing.mp4 60.7 MB
  2. Dataset Explanation Continue-en_US.srt 13.2 KB
  2. Dataset Explanation Continue.mp4 62.7 MB
  2. Feature Engineering Continue-en_US.srt 9.6 KB
  2. Feature Engineering Continue.mp4 86.7 MB
  3. Calculating Variance Inflation Factor Continue-en_US.srt 13.6 KB
  3. Calculating Variance Inflation Factor Continue.mp4 119.4 MB
  3. Data Transformation-en_US.srt 10.2 KB
  3. Data Transformation.mp4 53.3 MB
  3. Handling Missing Values-en_US.srt 10.9 KB
  3. Handling Missing Values.mp4 76.4 MB
  4. Handling Missing Values Continue-en_US.srt 9.7 KB
  4. Handling Missing Values Continue.mp4 76.2 MB
  4. Target Variable Splitting-en_US.srt 7.4 KB
  4. Target Variable Splitting.mp4 46 MB
  5. Exploratory Data Analysis-en_US.srt 8.7 KB
  5. Exploratory Data Analysis.mp4 58.2 MB
  6. Exploratory Data Analysis Continue-en_US.srt 11.1 KB
  6. Exploratory Data Analysis Continue.mp4 71.3 MB
  7. Correlation-en_US.srt 12.5 KB
  7. Correlation.mp4 93.8 MB
  Bonus Resources.txt 307.2 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 38 total files

Description


House Price Prediction using Linear Regression and Python

MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
Language: English | Size: 1.25 GB | Duration: 2h 47m
What you'll learn
Our course ensures that you will be able to think with a predictive mindset and understand well the basics of the techniques used in prediction.
Critical thinking is very important to validate models and interpret the results. Hence, our course material emphasizes on hardwiring this

Requirements
To get started with Predictive Modelling with Python a solid foundation in statistics is much appreciated. It takes a good amount of understanding to interpret those numbers to understand whether the numbers are adding up or not.
Along with the above-mentioned knowledge, one must know to code in Python.
Knowing SQL also acts as a complementary skillset.
Even if someone is not well equipped with the above-mentioned skill, it should not act as a hindrance as everything is possible with an honest effort and strong will.
Description
Predictive modeling is a field which has immense growth in line in due years to come due to the definite explosion of data that we are noticing. In the year 2017, it was forecasted by IBM that the demand for data scientists and analytical professionals will grow by 15% in the year 2020. Many companies have realized the importance of using predictive modeling for their business but currently, there is a shortage of skilled professionals. A substantial amount of salaries is offered to people with this skillset because of the nature of the job. The demand for qualified candidates is increasing at a significant rate. It is the right time to invest in learning for such a niche skill as the market for predictive analytics is not coming down any sooner.

It is the use of data and statistics to predict the outcome of the data models. This prediction finds its utility in almost all areas from sports, to TV ratings, corporate earnings, and technological advances. Predictive modeling is also called predictive analytics. With the help of predictive analytics, we can connect data to effective action about the current conditions and future events. Also, we can enable the business to exploit patterns and which are found in historical data to identify potential risks and opportunities before they occur. Python is used for predictive modeling because Python-based frameworks give us results faster and also help in the planning of the next steps based on the results.

Our course ensures that you will be able to think with a predictive mindset and understand well the basics of the techniques used in prediction. Critical thinking is very important to validate models and interpret the results. Hence, our course material emphasizes on hardwiring this similar kind of thinking ability. You will have good knowledge about the predictive modeling in python, linear regression, logistic regression.

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