# **Observations and Recomsndations**
## **1. Evaluation of Adolescent Weight problems dataset**
– 1.1 This file incorporates 43 entries with 4 Columns:-
– Location : State in USA
– VAlue : Peecent Worth Pattern
– 95 % Cl : Confidence Interval Values Starting from and to
– Pattern Dimension : The Dimension of the pattern
– 1.2 Null Values : We have now no null values within the dataset
## **2. Evaluation of Grownup Weight problems dataset**
– 2.1 This file incorporates 55 entries with 4 Columns:-
– Location : State in USA
– VAlue : Peecent Worth Pattern
– 95 % Cl : Confidence Interval Values Starting from and to
– Pattern Dimension : The Dimension of the pattern
– 2.2 Null Values : We have now no null values within the dataset
## **3. Evaluation of Diet Weight problems dataset**
– 3.1 This file incorporates 35042 entries with 14 Columns:-
– ‘YearStart’ : Begin of survey
– ‘YearEnd’ : Finish of Survey
– ‘LocationAbbr’ : Location at survey carried out
– ‘State’ : State survey carried out
– ‘Class’ : Class for which survey is carried out i.e Weight problems / Wt
– ‘Query’ : Questions requested like what number of p.c at school has weight problems
– ‘Data_Value_Type’ : Information Sort
– ‘Data_Value’ : Information Worth
– ‘Low_Confidence_Limit’ : Decrease restrict of confidence
– ‘High_Confidence_Limit ‘ : Decrease restrict of confidence
– ‘Sample_Size’ : Dimension of individuals pattern
– ‘Gender’ : Intercourse
– ‘Grade’ : Class of examine
– ‘Race_Ethnicit : Race of the pattern
– 3.2 Null Values : We have now fol values within the dataset:-
– Gender 30036
– Grade 25030
– Race_Ethnicity 17521
– Data_Value 9309
– Low_Confidence_Limit 9309
– High_Confidence_Limit 9309
– Sample_Size 9309
– 3.4 Dealing with of Lacking values within the dataset:-
– Since we all know that on this dataset our values lies between the Information Worth, higher and Decrease confidence stage. So within the dataset if all these three options are lacking then they don’t seem to be required and imputing these values might give us mistaken insights concerning the dataset, as these are the principle options.
– As we discovered that the lacking values in every column are greater than 50% so it’s not good observe to impute them.So we’ve got dropped the Columns, Race_Ethinicity, Gender and Class
– 3.5 Encoding of Categorical Values:
– **Query Column**
– ‘% of scholars in grades 9–12 who’ve weight problems’: 1,
– ‘% of scholars in grades 9–12 who’ve an obese classification’: 2,
– ‘% of scholars in grades 9–12 watching 3 or extra hours of tv every faculty day’: 3,
– ‘% of scholars in grades 9–12 who devour fruit lower than 1 time each day’: 4,
– ‘% of scholars in grades 9–12 who take part in each day bodily schooling’: 5,
– ‘% of scholars in grades 9–12 who devour greens lower than 1 time each day’: 6,
– ‘% of scholars in grades 9–12 who drank common soda/pop at the least one time per day’: 7,
– ‘% of scholars in grades 9–12 who obtain 1 hour or extra of moderate-and/or vigorous-intensity bodily exercise each day’: 8
– “**Class Column**”
– ‘Weight problems / Weight Standing’: 1,
– ‘Fruits and Greens’: 2,
– ‘Bodily Exercise’: 3,
– ‘Tv Viewing’: 4,
– ‘Sugar Drinks’: 5
– 3.6 Outliers Removing / Scaling of the dataset
– All of the outlires are visualize thorugh BoxPlot after which eliminated utilizing IQR Technique with 1.5 x Time higher and Decrease Restrict to the dataset
– The Information is scaled with MinMax Scler so, that the computuiona Time and value is decreased and the Accuracy of the mannequin is elevated.
– 3.7 Last Outcomes of the Diet Weight problems after making use of the linear Mannequin
– Root Imply Squared Error : 0.35
– R_Squared Worth : 0.99
## **4. Evaluation of Weight Weight problems dataset**
– 4.1 This file incorporates 2899 entries with 17 Columns:-
– ‘unit’: wether information is age oriented or not
– ‘classification1’ : Intercourse and race and Hispanic origin, Intercourse and age ,% of poverty stage, Race and Hispanic origin, Intercourse, Complete,
– ‘demographic_group’, ‘12 months’, ‘estimate’,
– ‘grade 1 weight problems (bmi from 30.0 to 34.9)’ : GradeI Weight problems
– ‘grade 2 weight problems (bmi from 35.0 to 39.9)’ : GradeII Weight problems
– ‘grade 3 weight problems (bmi better than or equal to 40.0)’ : GradeIII Weight problems
– ‘regular weight (bmi from 18.5 to 24.9)’ : GradeIV Weight problems
– weight problems (bmi better than or equal to 30.0)’ : GradeV Weight problems
– ‘obese or overweight (bmi better than or equal to 25.0)’ : GradeVI Weight problems
– ‘age_20–34 years’, ‘age_35–44 years’, ‘age_45–54 years’ : GradeVII Weight problems
– ‘age_55–64 years’, ‘age_65–74 years’, ‘age_75 years and over’ : GradeVIII Weight problems
– 4.2 Encode the dataset, clear the information, outlier detection and removing adopted by scaling of the information as finished in prevous dataset.
– Add new columns begin of the 12 months and finish of the 12 months for higher dealing with of the dataset
– Drop the 12 months column
– Encode the demographic_group column
– Encode the classification1 column
– Encode the unit column
– Take away the outliers from the dataset
– 4.5 Machine Studying Fashions for Classification Drawback
– Linear Regression (Accurcay Rating Acheived = 1)
– Random Forest Classifier (Accurcay Rating Acheived = 1)
## 5. **Conclusions and Suggestions:**
### 5.1 Adolescent Weight problems Dataset:
– **Conclusion:** The dataset gives data on weight problems charges amongst adolescents in numerous states of the USA.
– **Advice:** Additional evaluation needs to be performed to determine patterns and traits in adolescent weight problems charges, which will help in implementing focused interventions and applications to handle the difficulty.
### 5.2 Grownup Weight problems Dataset:
– **Conclusion:** The dataset incorporates data on weight problems charges amongst adults in numerous states of the USA.
– **Advice:** Conduct in-depth evaluation to know the prevalence of grownup weight problems in numerous states and develop methods and insurance policies to advertise wholesome life and fight weight problems.
### 5.3 Diet Weight problems Dataset:
– **Conclusion:** The dataset consists of a lot of entries and gives data on weight problems, weight loss plan, and bodily exercise.
– **Advice:** Because of important lacking values, warning needs to be exercised when imputing information. Deal with obtainable data and conduct analyses to discover components related to weight problems and their influence on completely different inhabitants teams.
### 5.4 Weight Weight problems Dataset:
– **Conclusion:** The dataset gives data on weight problems charges categorized by demographic teams, age ranges, and classifications.
– **Advice:** Conduct detailed evaluation to discover weight problems charges throughout completely different demographic teams, age ranges, and classifications. Apply machine studying fashions to foretell weight problems charges and determine necessary predictors.
## 6. **Basic Suggestions:**
– Conduct complete research utilizing the obtainable information to achieve insights into the causes and penalties of weight problems.
– Collaborate with healthcare suppliers, policymakers, and researchers to develop focused interventions and insurance policies to handle weight problems.
– Promote consciousness campaigns to teach the general public concerning the significance of a wholesome way of life and the dangers related to weight problems.
– Implement applications that concentrate on diet schooling, bodily exercise promotion, and entry to wholesome meals choices.
– Consider the effectiveness of interventions and make mandatory changes primarily based on the findings.
– Constantly monitor weight problems charges and traits to evaluate the influence of interventions and determine areas that require additional consideration.
– Foster interdisciplinary collaborations to deal with weight problems from a number of angles, together with healthcare, schooling, city planning, and policy-making.
– By following these suggestions, it’s attainable to make important progress in addressing the weight problems epidemic and bettering public well being outcomes.
