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sofieaasheim committed Nov 9, 2020
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import dash_core_components as dcc
import dash_html_components as html

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
data_url = 'https://raw.githubusercontent.com/sofieaasheim/supervised-learning-project/main/data/life-expectancy.csv'
external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"]
data_url = "https://raw.githubusercontent.com/sofieaasheim/supervised-learning-project/main/data/life-expectancy.csv"

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

server = app.server

"""
REGRESSION PREDICTION MODEL
The function linear_prediction_model makes a predictive model using linear regression and
data from the data set.
"""

# Import the entire data sets
df = pd.read_csv(data_url, sep=",")
df.drop(['Country', 'Year', 'Status'], axis=1, inplace=True)
df.drop(["Country", "Year", "Status"], axis=1, inplace=True)
df_regr = df[np.isfinite(df).all(1)]

all_parameters = [
'AdultMortality','InfantDeaths','Alcohol','PercentageExpenditure', 'HepatitisB',
'Measles','BMI','UnderFiveDeaths','Polio','TotalExpenditure', 'Diphtheria','HIVAIDS',
'GDP','Population','Thinness1_19','Thinness5_9','Income','Schooling'
]
"AdultMortality",
"InfantDeaths",
"Alcohol",
"PercentageExpenditure",
"HepatitisB",
"Measles",
"BMI",
"UnderFiveDeaths",
"Polio",
"TotalExpenditure",
"Diphtheria",
"HIVAIDS",
"GDP",
"Population",
"Thinness1_19",
"Thinness5_9",
"Income",
"Schooling",
]
parameter_names = [
'Adult mortality','Infant deaths','Alcohol','Percentage expenditure', 'Hepatitis B',
'Measles','BMI','Under-5 deaths','Polio','Total expenditure', 'Diphtheria','HIV/AIDS',
'GDP','Population','Thinness age 1 to 19','Thinness age 5 to 9','HDI income','Schooling'
]
"Adult mortality (per 1000 population)",
"Infant deaths (per 1000 population)",
"Alcohol consumption (pure alcohol in litres per capita)",
"Percentage expenditure on health of GDP per capita (%)",
"Hepatitis B immunization coverage among 1-year olds (%)",
"Measles cases (per 1000 population)",
"Average BMI of entire population",
"Under-5 deaths (per 1000 population)",
"Polio immunization coverage among 1-year olds (%)",
"Total expenditure on health (% of total government expenditure)",
"Diphtheria (DTP3) immunzation coverage among 1-year olds (%)",
"HIV/AIDS deaths (per 1000 HIV/AIDS live births 0-4 years)",
"GDP per capita (USD)",
"Population of the country",
"Thinness age 1 to 19 (%)",
"Thinness age 5 to 9 (%)",
"Human Development Index (HDI) (0-1)",
"Number of years of schooling",
]

# Parameters with linearity
parameter_list = ['Schooling', 'Income', 'AdultMortality']
parameter_list = ["Schooling", "Income", "AdultMortality"]

"""
REGRESSION PREDICTION MODEL
The function linear_prediction_model makes a predictive model using linear regression and
data from the data set.
"""


def linear_prediction_model(parameter_list, selected_values):
X = df_regr[parameter_list].round(2)
y = df_regr['LifeExpectancy'].round(2)
y = df_regr["LifeExpectancy"].round(2)

regr = linear_model.LinearRegression()
regr.fit(X, y)

prediction_result = regr.predict([selected_values])[0]
prediction_result = regr.predict([selected_values])[0]
return round(prediction_result, 2)


"""
CORRELATION MATRIX
The function make_correlation_matrix makes a heatmap showing all correlations
between the parameters in terms of the Pearson correlation coefficient.
"""


def make_correlation_matrix(df_regr):
correlation_df = df_regr[all_parameters].corr()
fig = go.Figure(
data=go.Heatmap(
z=correlation_df,
x=list(correlation_df.columns),
y=list(correlation_df.columns),
hoverongaps=False,
)
)
fig.update_layout(
height=600,
margin={"t": 20},
font_family="Helvetica",
)
return fig


""" LAYOUT """

app.layout = html.Div([
html.Div([], className="one column"),
html.Div([
html.H2('Life expectancy prediction'),
app.layout = html.Div(
[
html.Br(),
html.Br(),
html.Div([], className="one column"),
html.Div(
"This is a tool for predicting the life expectancy of the population in your country. The prediction"
+ "model is built from a multiple linear regression on a data set from WHO."
),
html.H5("Schooling"),
html.Div("Select the average number of schooling years:"),
dcc.Dropdown(
id='schooling',
options=[{'label': i, 'value': i} for i in range(0,22)],
value=0
),
html.H5("HDI"),
html.Div("Select your Human Development Index (HDI) in terms of income composition of resources:"),
dcc.Slider(
id='hdi-income',
min=0,
max=1,
step=0.01,
value=0,
marks={0:'0', 0.1:'0.1', 0.2:'0.2', 0.3:'0.3', 0.4:'0.4', 0.5:'0.5', 0.6:'0.6', 0.7:'0.7', 0.8:'0.8', 0.9:'0.9', 1:'1'}
),
html.H5("Adult mortality"),
html.Div("Select the adult mortality rate per 1000 population: "),
dcc.Slider(
id='adult-mortality',
min=0,
max=1000,
step=10,
value=0,
marks={0:'0', 100:'100', 200:'200', 300:'300', 400:'400', 500:'500', 600:'600', 700:'700', 800:'800', 900:'900', 1000:'1000'}
[
html.H2("Life expectancy prediction"),
html.Div(
[
"This is a tool for predicting the life expectancy of the population in your country. The prediction "
+ "model is built from a multiple linear regression on a data set from WHO found ",
html.A(
"here",
href="https://www.kaggle.com/kumarajarshi/life-expectancy-who",
),
".",
html.Br(),
html.Br(),
"After performing many tests, the parameters below turned out to be the most "
+ "statistically significant parameter combination for this predictive model. ",
html.Br(),
html.Br(),
"(Jeg tenker vi oppdaterer parameterne ut i fra hvilke resultater vi får av regresjonen, "
+ "de under er bare et foreløpig forslag.)",
]
),
html.Br(),
html.H5("Schooling"),
html.Div("Select the average number of schooling years:"),
dcc.Dropdown(
id="schooling",
options=[{"label": i, "value": i} for i in range(0, 22)],
value=0,
),
html.Br(),
html.H5("HDI"),
html.Div(
"Select your Human Development Index (HDI) in terms of income composition of resources:"
),
dcc.Slider(
id="hdi-income",
min=0,
max=1,
step=0.01,
value=0,
marks={
0: "0",
0.1: "0.1",
0.2: "0.2",
0.3: "0.3",
0.4: "0.4",
0.5: "0.5",
0.6: "0.6",
0.7: "0.7",
0.8: "0.8",
0.9: "0.9",
1: "1",
},
),
html.Br(),
html.H5("Adult mortality"),
html.Div("Select the adult mortality rate per 1000 population: "),
dcc.Slider(
id="adult-mortality",
min=0,
max=1000,
step=10,
value=0,
marks={
0: "0",
100: "100",
200: "200",
300: "300",
400: "400",
500: "500",
600: "600",
700: "700",
800: "800",
900: "900",
1000: "1000",
},
),
html.Br(),
html.H4("The predicted life expectancy in years is: "),
html.H3(id="prediction"),
],
style={"backgroundColor": "#F5F5F5", "padding": "0px 20px 20px 20px"},
className="five columns",
),
html.H4("The predicted life expectancy in years is: "),
html.H4(id='prediction'),
], className="five columns"),

html.Div([
html.H2('Correlation between a selected parmeter and the life expectancy'),
html.Div(
"This is a visualization of the raw data from the data sets. " +
"Select a parameter:"
[
html.H2("Raw data visualizations"),
html.H4(
"Correlation between a selected parmeter and the life expectancy"
),
html.Div("Select a parameter:"),
dcc.Dropdown(
id="select-parameter",
options=[
{"label": i, "value": j}
for i, j in zip(parameter_names, all_parameters)
],
value="AdultMortality",
),
html.Div(dcc.Graph(id="correlation-plot")),
html.H4("Correlations between all parameters (independent variables)"),
html.Div(
dcc.Graph(
id="correlation-matrix", figure=make_correlation_matrix(df_regr)
)
),
],
className="six columns",
),
dcc.Dropdown(
id='select-parameter',
options=[{'label': i, 'value': j} for i,j in zip(parameter_names, all_parameters)],
value='AdultMortality'
),
html.Div(dcc.Graph(id='correlation-plot'))
], className="six columns")
])
]
)

"""
CALLBACK FUNCTIONS
Thede function changes the values of the parameters used in the prediction, and
changes the plots
"""


@app.callback(
Output('prediction', 'children'),
Output("prediction", "children"),
[
Input('schooling', 'value'),
Input('hdi-income', 'value'),
Input('adult-mortality', 'value')
]
Input("schooling", "value"),
Input("hdi-income", "value"),
Input("adult-mortality", "value"),
],
)
def get_prediction_result(schooling, hdi_income, adult_mortality):
selected_values = [schooling, hdi_income, adult_mortality]
predicted_grade = linear_prediction_model(parameter_list, selected_values)
return predicted_grade


@app.callback(
Output('correlation-plot', 'figure'),
[
Input('select-parameter', 'value')
]
Output("correlation-plot", "figure"), [Input("select-parameter", "value")]
)
def make_correlation_graph(select_parameter):
X = df_regr[select_parameter].round(2)
y = df_regr['LifeExpectancy'].round(2)
fig = go.Figure(data=go.Scatter(x=X, y=y, mode='markers'))
y = df_regr["LifeExpectancy"].round(2)
fig = go.Figure(data=go.Scatter(x=X, y=y, mode="markers"))
fig.update_layout(
yaxis_title="Life expectancy",
xaxis_title=f"{select_parameter}"
xaxis_title=f"{select_parameter}",
height=600,
margin={"t": 20, "b": 20},
font_family="Helvetica",
)
return fig

if __name__ == '__main__':
app.run_server(debug=True)

if __name__ == "__main__":
app.run_server(debug=True)

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