Predictive Analytics Jobs
Predictive Analytics is a technique that uses statistical models, machine learning, and artificial intelligence to analyze data and generate insights that can be used to make predictions about the future. Predictive Analytics makes it possible to quantify the future in terms of probabilities and can help businesses to make better decisions. A Predictive Analytics Expert is someone who is skilled and experienced in using a variety of predictive models and specialized software, in order to develop complex statistical models that can be used to gain insights into data sets.
Here's some projects that our se Predictive Analytics Experts made real:
- Gathered data from sources, performed data wrangling, text mining and mapGIS while developing Machine Learning Models.
- Used SAS Enterprise Miner to explore data sets and explain results in detail.
- Benefitted clients with predict temperature through the usage of multiple Machine Learning frameworks.
- Utilized Big Data for the creation of predictive models for market analysis reports.
- Created complex algorithms for matters such as prediction technique, analysis, reverse calculations, pattern finding, model building and more.
- Trained several data mining models to generate stock predictions accurately.
- Implemented solutions to clean datasets as well as create predictive modelling solutions with rstudio.
- Developed various binary Target predictions with associated high accuracy probabilities.
At Freelancer.com we have expert Predictive Analytics professionals who can help you harness the power of Predictive Analytics by providing you with the highest quality solutions tailored for your needs. Post your project now on Freelancer.com and hire a Predictive Analytics Expert!На основании 1,559 отзывов клиентов, рейтинг Predictive Analytics Experts составляет 4.86 из 5 звездочек.
Нанять Predictive Analytics Experts
1 Part 1 - Building up a basic predictive model Load the dataset into a pandas dataframe and carry out the following tasks. Organise your code bearing in mind robustness and maintainability: 1. Data cleaning and transformation: If you have a closer look at the dataset, you will see that there are lots of missing values. They need be treated appropriately but in the first instance, we will take an aggressive approach to dealing with them. • Show the shape of the dataset • Rename incorrectly formatted column names (e.g. SALEnPRICE) • Create list of categorical variables and another for the numerical variables • For each numerical column, remove the ',' the '$' for the sale price, and then convert them to numeric. • Conve...