i can provide the graphic or code, and i need sb. to analysis the data for me

there are two datasets that you need to analysis for, here are the link where you can find the data

Seed data:https://archive.ics.uci.edu/ml/datasets/seeds

Automobile data:https://archive.ics.uci.edu/ml/datasets/Automobile

Topics for project:

1) How to compare multiple labels with respect to one single feature? Each label is attached to a 1-dim dataset of feature measurements. Datasets: Seed.

2) How to see intrinsic differences among multiple labels with respect to multiple features? Each label is attached to a K-dim dataset of feature measurements. Datasets: Seed.

3) How to deal with categorical features? Dataset: Automobile.

4) How to measure associative relations between a categorical response variable and multiple covariate features. Datasets: Seedand Automobile datasets.

here are the code: you may run this in JupyterLab in order to see the graphic

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from statsmodels.distributions.empirical_distribution import ECDF

import seaborn as sns

from matplotlib.pyplot import cm

from pyitlib import discrete_random_variable as drv

from plotnine import *

from statistics import *

from collections import Counter

import warnings

warnings.filterwarnings(“ignore”)

seed_df = pd.read_csv(‘seeds_dataset.txt’, delim_whitespace=True, header = None)

seed_df.columns = [“area”, “perimeter”, “compactness”, “length of kernel”, ” width of kernel”, “asymmetry coefficient”, “length of kernel groove”, “wheat”]

seed_df.corr()

sns.heatmap(seed_df.iloc[:,0:7].corr())

sns.clustermap(seed_df.iloc[:,0:7].corr())

fig, axs = plt.subplots(7, 3, figsize=(15, 10))

fig.subplots_adjust(top = 2.5, bottom = 0.5, wspace = 0.3)

axs = axs.ravel()

for i in range(1, len(seed_df.columns)):

ecdf = ECDF(seed_df.iloc[:,i-1])

x = np.linspace(min(seed_df.iloc[:,i-1]), max(seed_df.iloc[:,i-1]))

y = ecdf(x)

axs[3*i-3].step(x, y)

axs[3*i-3].set_title(f”Empirical CDF for {seed_df.columns[i-1]}”)

seed_df.pivot_table(values=seed_df.columns[i-1], index=seed_df.index, columns=[‘wheat’]).plot.hist(bins=50, stacked=True, ax=axs[3*i-2])

axs[3*i-2].set_title(f”Gapped Histogram for {seed_df.columns[i-1]}”)

axs[3*i-1].plot(seed_df.iloc[:,i-1])

axs[3*i-1].set_title(f”Line plot for {seed_df.columns[i-1]}”)

sns.pairplot(seed_df, hue = “wheat”)

def entropy(Y):

unique, count = np.unique(Y, return_counts=True, axis=0)

prob = count/len(Y)

en = np.sum((-1)*prob*np.log2(prob))

return en

#Joint Entropy

# H(Y;X)

def jEntropy(Y,X):

YX = np.c_[Y,X]

return entropy(YX)

#Conditional Entropy

## conditional entropy = Joint Entropy – Entropy of X

## H(Y|X) = H(Y;X) – H(X)

def cEntropy(Y, X):

return jEntropy(Y, X) – entropy(X)

#Mutual Information

#Mutual Information, I(Y;X) = H(Y) – H(Y|X)

def mutual_info(Y, X):

return entropy(Y) – cEntropy(Y,X)

feature_selection.mutual_info_classif(seed_df.iloc[:,0:7], seed_df.iloc[:,-1], discrete_features=’auto’)

def mutualy_table(df):

n = len(df.columns) – 1

ce_df = pd.DataFrame(np.zeros((n, n)))

for i in range(0, n):

for j in range(0, n):

ce_df[i][j] = mutual_info(seed_df.iloc[:, i], seed_df.iloc[:, j])

ce_df.columns = df.columns[:-1]

ce_df.index = df.columns[:-1]

return ce_df

print(“Mutual Information table is:”)

mutualy_table(seed_df)

auto_df = pd.read_csv(‘imports-85.data’, header = None)

auto_df.columns =[“symboling”, “normalized-losses”, “make”, “fuel-type”, “aspiration”, “num-of-doors”, “body-style”, “drive-wheels”,

“engine-location”, “wheel-base”, “length”, “width”, “height”, “curb-weight”, “engine-type”, “num-of-cylinders”,

“engine-size”, “fuel-system”, “bore”, “stroke”, “compression-ratio”, “horsepower”, “peak-rpm”, “city-mpg”, “highway-mpg”, “price”]

def count_na(df):

for col in df.columns:

l = df[df[col] == “?”].shape[0]

if l != 0:

print(f”{col} has {l} missing value”)

# which is same as the document said

count_na(auto_df)

def replace_na(df):

df_new = df.copy()

for col in [“normalized-losses”, “bore”, “stroke”, “horsepower”, “peak-rpm”, “price”]:

i = df[df[col] == “?”].index

j = list(set(range(0, df[col].shape[0])) – set(i))

df_new.loc[i, col] = mean(pd.to_numeric(df.loc[j, col]))

for col in [“num-of-doors”]:

i = df[df[col] == “?”].index

j = list(set(range(0, df[col].shape[0])) – set(i))

df_new.loc[i, col] = max(Counter(df.loc[j, col]))

return(df_new)

auto_df_new = replace_na(auto_df)

auto_df_new

def change_dtype(df):

l1 = [3, 4, 5, 6, 7, 8, 9, 15, 16, 18]

for i in l1:

df.iloc[:, i-1] = pd.Categorical(df.iloc[:, i-1])

l2 = list(set(range(1, 27)) – set(l1) – set([1]))

for j in l2:

df.iloc[:, j-1] = pd.to_numeric(df.iloc[:, j-1])

return df

change_dtype(auto_df_new)

auto_df_new_dummy = pd.get_dummies(auto_df_new)

sns.pairplot(auto_df, hue = “symboling”)

the attachments are the reference report example and the project template

Individual write-up intervention project of a public education/awareness campaign to be completed. This brief, written paper should include the following elements:

1. Introduction/Recap and Outreach Description – Describe your topic (West Nile Virus) and what you propose for your community outreach/education campaign.
2. Goals and Objectives – Being as specific as possible identify the three main Goals of your project and the Objectives you will use to accomplish them, for example:
   1. 1. Goal: Increase Recycling in the Dormitories by 20%

i. Objective 1: Education campaign with flyers

ii. Objective 2: Pizza night competition

iii. Objective 3: Change collection bin location and size

1. Personal Engagement – Reflecting on what you learn from researching the issue and analyzing it from a systems analysis perspective, develop some type of personal engagement effort around this topic. Encouraging personal engagement in the target audience could take a variety of forms:
   1. Personal actions
   2. Group actions
   3. Policy changes
      1. The more specific you can make the suggestions, the more points you will receive. For example, provide some information on clearinghouses that help people take action, actual websites were they can write to policy makers, numerical examples of the impacts their behavior changes could have, etc.
2. Funding Needs –Provide an estimate of the funding that would be required to complete your project. Almost all projects will have at least the following budget categories:
   1. Salaries/Benefits
   2. Equipment
   3. Supplies and Services
   4. Travel

The Semester Project Will Be Evaluated On These Criteria

Intellectual Content – How well researched and referenced is this effort? Is a systems analysis approach clearly utilized to both analyze the problem and develop a solution? How creative was the systems analysis of the issues and was this analysis used to offer potential solutions that could bring about meaningful change? Was the effort well-written and clear?

Integration of system thinking and climate change topic with health outcomes.

Professional, Accessible, and Clear Explanations and Writing – Were the final products delivered in an effective and professional manner? Is the content clear? Were all of the required content areas addressed?

Meaningful Personal Engagement – Was the project meaningfully engaging? Is it apparent that some effort and care went into the development and delivery of the project? In other words, was the effort genuine and meaningful?

Timeliness of Completion and Delivery – Is the project on time? This will impact all other evaluation areas.

The results in Table 1 are key amounts from the SEC 10-K of (your company’s name).

b.Balance Sheet Results. Using the results from the Balance Sheet items, comment on your company’s results. You should discuss and compare at least three of the items from your balance sheet results. Before presenting your analysis, define the terms you are discussing and cite a source like your textbook, to demonstrate that you know the meaning of the terms you are discussing. For instance, if you discuss current liabilities, you should explain what that term means. You can also discuss other balance sheet items like non-current assets or long-term liabilities if you want to, but you should also define any additional terms.

c.Income Statement Results. Using the results from the Income Statement items, comment on your company’s results. As mentioned above, define the terms you discuss, cite a source, and discuss at least three of the items.

Health Informatics is a rapidly evolving field with a wealth of rich, current, credible and helpful resources on the Internet and accessible via mobile health apps. So, have fun some fun with this assignment by browsing and learning about health informatics-enabled resources. Then, select several web sites and develop a review of what you find helpful content in each selected site. Include a blending of credible government, non-profit, for profit, educational, and consumer-driven sites to provide a rich diversity of perspectives. Additionally, include reviews of mobile heath apps and other healthcare technologies.

Target your Course Project website to be a reference for a specific audience and include a brief introduction. For example, you may focus this as a guide for:

– Fellow public health, health administration or nursing students,

– Professional groups such as for nurses, healthcare administrators, physicians, etc., or

– Health care consumer or someone working somewhere in the healthcare ecosystem.

– Specific groups of consumers (kids, seniors) or people with a specific health condition (diabetes, heart condition)

Note: The draft version should be 90% complete in order for your instructor to provide you with sufficient feedback and any changes/additions that need to be made by the final version due in week 4.