代做PM2.5 dataset、代写Python设计、代做Exploratory Analysis、Python代写

日期：2018-12-05 11:13

Project Proposal

Exploratory Analysis & Prediction on PM2.5 dataset

Liu, Jianke - liu.jiank@husky.neu.edu

   Krishnamoorthy, Yeshwin - krishnamoorthy.y@husky.neu.edu

Ramakrishnan, Ganapathy Subramaniam - ramakrishnan.ga@husky.neu.edu

Objective

The objective is to analyze the impact of meteorological factors like temperature, pressure and wind direction affecting the aggregation, diffusion and spread of PM2.5 levels in Beijing, China and to predict them in the future.

Approach

Firstly, using data recorded by the US Embassy in Beijing for the years 2010-2014, we understand and define the problem by having a look at the input parameters (temperature, pressure, dew factor, wind direction, timeline of data, etc.) and the output parameter (PM2.5 concentration).

Secondly, we analyze and prepare the data by preprocessing (data cleansing, formatting, and sampling) and transformation (scaling and aggregation).

Thirdly, we choose the type of machine learning algorithm to use. Since we have both the input and output data, we plan to use supervised machine learning algorithms like native Bayes, linear regression and neural networks.

Next, we partition the data into three subsets – training, test and validation set. The proportion of a training and a test set is usually 80 to 20 percent respectively. We split the training set again, and use its 20 percent to form a validation set.

Finally, we predict the PM2.5 level the best model based on testing the data using machine-learning techniques. To evaluate the result, we may compare our results with the real value we get from the internet.

Data Acquisition

We find the datasets mainly from https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data.

Coding

Languages – Python

Libraries - Pandas, NumPy, TensorFlow, Keras

Timeline

Oct 19 – Oct 26.      Initial analysis and data preprocessing

Oct. 27 – Nov. 12    Selection of model and training the data

Nov. 13 – Nov. 28   Evaluation and prediction

Nov 29 – Dec 2        Final preparation of report and presentation

Team-member roles

XXX: Algorithm implementation and coding

XXX: Coding and testing data

XXX: Analysis, testing and documentation