diff --git a/.github/workflows/cml.yaml b/.github/workflows/cml.yaml
index 5898fbd..4620c16 100644
--- a/.github/workflows/cml.yaml
+++ b/.github/workflows/cml.yaml
@@ -16,10 +16,10 @@ jobs:
           python myTraining.py
 
           echo "## Model score" > report.md
-          cat score.txt >> report.md
+          cat results/score_monitoring.txt >> report.md
 
           echo "## Data visual" >> report.md
-          cml-publish report.png --md >> report.md
+          cml-publish results/report_threshold.png --md >> report.md
           
           cml-send-comment report.md
 
diff --git a/data.csv b/data/data.csv
similarity index 97%
rename from data.csv
rename to data/data.csv
index c4b2605..02ac2ec 100644
--- a/data.csv
+++ b/data/data.csv
@@ -1,5435 +1,5435 @@
-Breathing Problem,Fever,Dry Cough,Sore throat,Running Nose,Asthma,Chronic Lung Disease,Headache,Heart Disease,Diabetes,Hyper Tension,Fatigue ,Gastrointestinal ,Abroad travel,Contact with COVID Patient,Attended Large Gathering,Visited Public Exposed Places,Family working in Public Exposed Places,Wearing Masks,Sanitization from Market,COVID-19
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+1,0,0,1,1,1,0,0,0,0,0,1,0,0,1,1,1,0,0,0,1
+1,1,1,1,1,0,1,0,0,0,1,1,0,0,1,1,1,0,0,0,1
+1,0,0,0,0,0,0,1,1,0,0,0,0,0,1,1,1,0,0,0,1
+1,0,0,0,0,0,0,1,1,0,1,1,0,0,1,1,1,0,0,0,1
+1,0,1,1,0,1,0,0,1,1,1,0,0,0,1,1,1,0,0,0,1
+1,1,0,1,0,0,1,0,0,0,0,1,0,0,1,1,1,0,0,0,1
+0,0,1,0,0,1,1,0,1,1,0,0,1,0,1,1,1,0,0,0,1
+1,1,1,1,1,1,1,0,0,1,1,0,1,0,1,1,1,0,0,0,1
+1,0,0,0,1,0,1,1,0,1,0,1,1,0,1,1,1,0,0,0,1
+0,0,1,1,1,0,0,1,1,0,0,0,1,0,1,1,1,0,0,0,1
+0,1,1,1,0,1,0,0,0,1,0,1,0,0,1,1,1,0,0,0,1
+1,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1
+1,1,1,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1
+1,1,0,1,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0,1
+1,1,1,0,1,1,0,1,0,1,1,1,0,0,0,0,0,0,0,0,1
+1,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1
+1,1,1,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1
+1,1,0,1,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0,1
+1,1,1,0,1,1,0,1,0,1,1,1,0,0,0,0,0,0,0,0,1
+1,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0
+1,1,1,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0
+1,1,1,0,1,1,0,1,0,1,1,1,0,0,0,0,0,0,0,0,0
diff --git a/data/data_preparation/data_preparation.py b/data/data_preparation/data_preparation.py
new file mode 100644
index 0000000..a951364
--- /dev/null
+++ b/data/data_preparation/data_preparation.py
@@ -0,0 +1,107 @@
+# Librerie utili per l'analisi dei dati
+import pandas as pd
+import numpy as np
+import matplotlib
+import seaborn as sns
+import matplotlib.pyplot as plt
+from numpy.random.mtrand import seed
+
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn import tree
+
+# Configurazione dello stile dei grafici
+sns.set(context='notebook', style='darkgrid', palette='colorblind', font='sans-serif', font_scale=1, rc=None)
+matplotlib.rcParams['figure.figsize'] =[8,8]
+matplotlib.rcParams.update({'font.size': 15})
+matplotlib.rcParams['font.family'] = 'sans-serif'
+
+# --- Preparazione dei dati ---
+# Caricamento del dataset
+
+def data_split(data, ratio):
+    np.random.seed(42)
+    shuffled = np.random.permutation(len(data))
+    test_set_size = int(len(data) * ratio)
+    test_indices = shuffled[:test_set_size]
+    train_indices = shuffled[test_set_size:]
+    return data.iloc[train_indices], data.iloc[test_indices]
+
+if __name__== "__main__":
+
+    # Read The Data
+    covid = pd.read_csv('data/data.csv')
+    train, test = data_split(covid, 0.2)
+    X_train = train[['Breathing Problem', 'Fever', 'Dry Cough', 'Sore throat',
+       'Running Nose', 'Asthma', 'Chronic Lung Disease', 'Headache',
+       'Heart Disease', 'Diabetes', 'Hyper Tension', 'Fatigue ',
+       'Gastrointestinal ', 'Abroad travel', 'Contact with COVID Patient',
+       'Attended Large Gathering', 'Visited Public Exposed Places',
+       'Family working in Public Exposed Places', 'Wearing Masks',
+       'Sanitization from Market']].to_numpy()
+
+    X_test = test[['Breathing Problem', 'Fever', 'Dry Cough', 'Sore throat',
+       'Running Nose', 'Asthma', 'Chronic Lung Disease', 'Headache',
+       'Heart Disease', 'Diabetes', 'Hyper Tension', 'Fatigue ',
+       'Gastrointestinal ', 'Abroad travel', 'Contact with COVID Patient',
+       'Attended Large Gathering', 'Visited Public Exposed Places',
+       'Family working in Public Exposed Places', 'Wearing Masks',
+       'Sanitization from Market']].to_numpy()
+
+    Y_train = train[['COVID-19']].to_numpy().reshape(4348,)
+    Y_test = test[['COVID-19']].to_numpy().reshape(1086,)
+
+# Questo metodo stampa le informazioni su un DataFrame, inclusi l'indice dtype e le colonne, i valori non null e l'utilizzo della memoria.
+# Scrittura delle info su file.txt
+with open('data/data_preparation/info.txt', 'w') as f:
+    covid.info(buf=f)
+
+
+# Verifica dei dati mancanti
+missing_values=covid.isnull().sum() # valori mancanti
+percent_missing = covid.isnull().sum()/covid.shape[0]*100 # valori mancanti %
+value = {
+    'missing_values ':missing_values,
+    'percent_missing %':percent_missing  
+}
+frame=pd.DataFrame(value)
+frame.to_csv('data/data_preparation/missing_value.csv') # salvataggio su un file.csv per renderlo leggibile
+
+
+# Genera statistiche descrittive
+covid.describe().to_csv("data/data_preparation/dataset_statics.csv") # salvataggio su un file.csv per renderlo leggibile
+
+# --- Visualizzazione dei dati ---
+# COVID-19
+# sns_plot = sns.countplot(x='COVID-19', data=covid)
+# figure = sns_plot.get_figure()
+# figure.savefig('data/data_preparation/data_viz/COVID-19.png', dpi = 400)
+
+# Breathing Problem 
+# sns_breathing = sns.countplot(x='Breathing Problem',hue='COVID-19',data=covid)
+# figure1 = sns_breathing.get_figure()
+# figure1.savefig('data/data_preparation/data_viz/BreathingProblem.png', dpi = 400)
+
+# Fever 
+# sns_fever = sns.countplot(x='Fever', hue='COVID-19', data=covid)
+# figure2 = sns_fever.get_figure()
+# figure2.savefig('data/data_preparation/data_viz/Fever.png', dpi = 400)
+
+# Dry Cough
+# sns_dry = sns.countplot(x='Dry Cough',hue='COVID-19',data=covid)
+# figure3 = sns_dry.get_figure()
+# figure3.savefig('data/data_preparation/data_viz/dry.png', dpi = 400)
+
+# Sore Throat
+# sns_sore = sns.countplot(x='Sore throat',hue='COVID-19',data=covid)
+# figure4 = sns_sore.get_figure()
+# figure4.savefig('data/data_preparation/data_viz/sore.png', dpi = 400)
+
+
+
+
+
diff --git a/data/data_preparation/data_viz/BreathingProblem.png b/data/data_preparation/data_viz/BreathingProblem.png
new file mode 100644
index 0000000..8a310dc
Binary files /dev/null and b/data/data_preparation/data_viz/BreathingProblem.png differ
diff --git a/data/data_preparation/data_viz/COVID-19.png b/data/data_preparation/data_viz/COVID-19.png
new file mode 100644
index 0000000..c47f7e8
Binary files /dev/null and b/data/data_preparation/data_viz/COVID-19.png differ
diff --git a/data/data_preparation/data_viz/Fever.png b/data/data_preparation/data_viz/Fever.png
new file mode 100644
index 0000000..6d8a290
Binary files /dev/null and b/data/data_preparation/data_viz/Fever.png differ
diff --git a/data/data_preparation/data_viz/dry.png b/data/data_preparation/data_viz/dry.png
new file mode 100644
index 0000000..d8e893a
Binary files /dev/null and b/data/data_preparation/data_viz/dry.png differ
diff --git a/data/data_preparation/data_viz/sore.png b/data/data_preparation/data_viz/sore.png
new file mode 100644
index 0000000..062cd65
Binary files /dev/null and b/data/data_preparation/data_viz/sore.png differ
diff --git a/dataset_statics.csv b/data/data_preparation/dataset_statics.csv
similarity index 99%
rename from dataset_statics.csv
rename to data/data_preparation/dataset_statics.csv
index 48de1a0..a40a17b 100644
--- a/dataset_statics.csv
+++ b/data/data_preparation/dataset_statics.csv
@@ -1,9 +1,9 @@
-,Breathing Problem,Fever,Dry Cough,Sore throat,Running Nose,Asthma,Chronic Lung Disease,Headache,Heart Disease,Diabetes,Hyper Tension,Fatigue ,Gastrointestinal ,Abroad travel,Contact with COVID Patient,Attended Large Gathering,Visited Public Exposed Places,Family working in Public Exposed Places,Wearing Masks,Sanitization from Market,COVID-19
-count,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0
-mean,0.6661759293338241,0.7863452337136547,0.7926021347073978,0.7274567537725433,0.5432462274567538,0.4626426205373574,0.47202797202797203,0.5034965034965035,0.4642988590357011,0.4762605815237394,0.49006256900993744,0.5191387559808612,0.4694516010305484,0.45104895104895104,0.5016562384983437,0.4619065145380935,0.5189547294810453,0.41626794258373206,0.0,0.0,0.8065881486934119
-std,0.4716211327739574,0.4099235665965471,0.40548026751388566,0.44530878904756294,0.49817209342489355,0.4986483574853146,0.4992628934027898,0.5000337861645077,0.4987696953792399,0.4994820831364206,0.4999472415983911,0.49967955300634165,0.4991118498366407,0.49764381725600054,0.5000432695898289,0.4985926537423099,0.4996865689546946,0.49298444924353374,0.0,0.0,0.39500939378839356
-min,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-25%,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
-50%,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0
-75%,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0
-max,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0
+,Breathing Problem,Fever,Dry Cough,Sore throat,Running Nose,Asthma,Chronic Lung Disease,Headache,Heart Disease,Diabetes,Hyper Tension,Fatigue ,Gastrointestinal ,Abroad travel,Contact with COVID Patient,Attended Large Gathering,Visited Public Exposed Places,Family working in Public Exposed Places,Wearing Masks,Sanitization from Market,COVID-19
+count,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0
+mean,0.6661759293338241,0.7863452337136547,0.7926021347073978,0.7274567537725433,0.5432462274567538,0.4626426205373574,0.47202797202797203,0.5034965034965035,0.4642988590357011,0.4762605815237394,0.49006256900993744,0.5191387559808612,0.4694516010305484,0.45104895104895104,0.5016562384983437,0.4619065145380935,0.5189547294810453,0.41626794258373206,0.0,0.0,0.8065881486934119
+std,0.4716211327739574,0.4099235665965471,0.40548026751388566,0.44530878904756294,0.49817209342489355,0.4986483574853146,0.4992628934027898,0.5000337861645077,0.4987696953792399,0.4994820831364206,0.4999472415983911,0.49967955300634165,0.4991118498366407,0.49764381725600054,0.5000432695898289,0.4985926537423099,0.4996865689546946,0.49298444924353374,0.0,0.0,0.39500939378839356
+min,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+25%,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+50%,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0
+75%,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0
+max,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0
diff --git a/data/data_preparation/info.txt b/data/data_preparation/info.txt
new file mode 100644
index 0000000..b26128c
--- /dev/null
+++ b/data/data_preparation/info.txt
@@ -0,0 +1,28 @@
+<class 'pandas.core.frame.DataFrame'>
+RangeIndex: 5434 entries, 0 to 5433
+Data columns (total 21 columns):
+ #   Column                                   Non-Null Count  Dtype
+---  ------                                   --------------  -----
+ 0   Breathing Problem                        5434 non-null   int64
+ 1   Fever                                    5434 non-null   int64
+ 2   Dry Cough                                5434 non-null   int64
+ 3   Sore throat                              5434 non-null   int64
+ 4   Running Nose                             5434 non-null   int64
+ 5   Asthma                                   5434 non-null   int64
+ 6   Chronic Lung Disease                     5434 non-null   int64
+ 7   Headache                                 5434 non-null   int64
+ 8   Heart Disease                            5434 non-null   int64
+ 9   Diabetes                                 5434 non-null   int64
+ 10  Hyper Tension                            5434 non-null   int64
+ 11  Fatigue                                  5434 non-null   int64
+ 12  Gastrointestinal                         5434 non-null   int64
+ 13  Abroad travel                            5434 non-null   int64
+ 14  Contact with COVID Patient               5434 non-null   int64
+ 15  Attended Large Gathering                 5434 non-null   int64
+ 16  Visited Public Exposed Places            5434 non-null   int64
+ 17  Family working in Public Exposed Places  5434 non-null   int64
+ 18  Wearing Masks                            5434 non-null   int64
+ 19  Sanitization from Market                 5434 non-null   int64
+ 20  COVID-19                                 5434 non-null   int64
+dtypes: int64(21)
+memory usage: 891.6 KB
diff --git a/Missingvalue.csv b/data/data_preparation/missing_value.csv
similarity index 95%
rename from Missingvalue.csv
rename to data/data_preparation/missing_value.csv
index 80c6793..83b8f15 100644
--- a/Missingvalue.csv
+++ b/data/data_preparation/missing_value.csv
@@ -1,22 +1,22 @@
-,missing_values ,percent_missing %
-Breathing Problem,0,0.0
-Fever,0,0.0
-Dry Cough,0,0.0
-Sore throat,0,0.0
-Running Nose,0,0.0
-Asthma,0,0.0
-Chronic Lung Disease,0,0.0
-Headache,0,0.0
-Heart Disease,0,0.0
-Diabetes,0,0.0
-Hyper Tension,0,0.0
-Fatigue ,0,0.0
-Gastrointestinal ,0,0.0
-Abroad travel,0,0.0
-Contact with COVID Patient,0,0.0
-Attended Large Gathering,0,0.0
-Visited Public Exposed Places,0,0.0
-Family working in Public Exposed Places,0,0.0
-Wearing Masks,0,0.0
-Sanitization from Market,0,0.0
-COVID-19,0,0.0
+,missing_values ,percent_missing %
+Breathing Problem,0,0.0
+Fever,0,0.0
+Dry Cough,0,0.0
+Sore throat,0,0.0
+Running Nose,0,0.0
+Asthma,0,0.0
+Chronic Lung Disease,0,0.0
+Headache,0,0.0
+Heart Disease,0,0.0
+Diabetes,0,0.0
+Hyper Tension,0,0.0
+Fatigue ,0,0.0
+Gastrointestinal ,0,0.0
+Abroad travel,0,0.0
+Contact with COVID Patient,0,0.0
+Attended Large Gathering,0,0.0
+Visited Public Exposed Places,0,0.0
+Family working in Public Exposed Places,0,0.0
+Wearing Masks,0,0.0
+Sanitization from Market,0,0.0
+COVID-19,0,0.0
diff --git a/dvc.lock b/dvc.lock
index a9d448a..92c5823 100644
--- a/dvc.lock
+++ b/dvc.lock
@@ -3,13 +3,13 @@ stages:
   train:
     cmd: python myTraining.py
     deps:
-    - path: data.csv
+    - path: data/data.csv
       md5: 02b3a4aa918c639fb02b6d3cd98914c4
       size: 234007
     - path: myTraining.py
       md5: d8452f7c93722709dd9bf2069c4ae3ee
       size: 4770
     outs:
-    - path: metrics.json
+    - path: results/metrics.json
       md5: bb7d10954900192c72c9dd340b3dc570
       size: 131
diff --git a/dvc.yaml b/dvc.yaml
index e763201..83beb9f 100644
--- a/dvc.yaml
+++ b/dvc.yaml
@@ -3,7 +3,7 @@ stages:
     cmd: python myTraining.py
     deps:
     - myTraining.py
-    - data.csv
+    - data/data.csv
     metrics:
-    - metrics.json:
+    - results/metrics.json:
         cache: false
diff --git a/analysis.py b/models/accuracy.py
similarity index 68%
rename from analysis.py
rename to models/accuracy.py
index 2c6cf80..30b3965 100644
--- a/analysis.py
+++ b/models/accuracy.py
@@ -1,147 +1,101 @@
-# Librerie utili per l'analisi dei dati
-import pandas as pd
-import numpy as np
-import matplotlib
-import seaborn as sns
-import matplotlib.pyplot as plt
-from numpy.random.mtrand import seed
-
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.linear_model import LogisticRegression
-from sklearn.ensemble import GradientBoostingRegressor
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.naive_bayes import GaussianNB
-from sklearn.metrics import mean_squared_error, r2_score
-from sklearn import tree
-
-# Configurazione dello stile dei grafici
-sns.set(context='notebook', style='darkgrid', palette='colorblind', font='sans-serif', font_scale=1, rc=None)
-matplotlib.rcParams['figure.figsize'] =[8,8]
-matplotlib.rcParams.update({'font.size': 15})
-matplotlib.rcParams['font.family'] = 'sans-serif'
-
-# --- Preparazione dei dati ---
-# Caricamento del dataset
-
-def data_split(data, ratio):
-    np.random.seed(42)
-    shuffled = np.random.permutation(len(data))
-    test_set_size = int(len(data) * ratio)
-    test_indices = shuffled[:test_set_size]
-    train_indices = shuffled[test_set_size:]
-    return data.iloc[train_indices], data.iloc[test_indices]
-
-if __name__== "__main__":
-
-    # Read The Data
-    covid = pd.read_csv('data.csv')
-    train, test = data_split(covid, 0.2)
-    X_train = train[['Breathing Problem', 'Fever', 'Dry Cough', 'Sore throat',
-       'Running Nose', 'Asthma', 'Chronic Lung Disease', 'Headache',
-       'Heart Disease', 'Diabetes', 'Hyper Tension', 'Fatigue ',
-       'Gastrointestinal ', 'Abroad travel', 'Contact with COVID Patient',
-       'Attended Large Gathering', 'Visited Public Exposed Places',
-       'Family working in Public Exposed Places', 'Wearing Masks',
-       'Sanitization from Market']].to_numpy()
-
-    X_test = test[['Breathing Problem', 'Fever', 'Dry Cough', 'Sore throat',
-       'Running Nose', 'Asthma', 'Chronic Lung Disease', 'Headache',
-       'Heart Disease', 'Diabetes', 'Hyper Tension', 'Fatigue ',
-       'Gastrointestinal ', 'Abroad travel', 'Contact with COVID Patient',
-       'Attended Large Gathering', 'Visited Public Exposed Places',
-       'Family working in Public Exposed Places', 'Wearing Masks',
-       'Sanitization from Market']].to_numpy()
-
-    Y_train = train[['COVID-19']].to_numpy().reshape(4348,)
-    Y_test = test[['COVID-19']].to_numpy().reshape(1086,)
-
-# Questo metodo stampa le informazioni su un DataFrame, inclusi l'indice dtype e le colonne, i valori non null e l'utilizzo della memoria.
-#covid.info()
-
-# Verifica dei dati mancanti
-missing_values=covid.isnull().sum() # valori mancanti
-percent_missing = covid.isnull().sum()/covid.shape[0]*100 # valori mancanti %
-value = {
-    'missing_values ':missing_values,
-    'percent_missing %':percent_missing  
-}
-frame=pd.DataFrame(value)
-frame.to_csv('Missingvalue.csv') # salvataggio su un file.csv per renderlo leggibile
-
-
-# Genera statistiche descrittive
-#covid.describe().to_csv("dataset_statics.csv") # salvataggio su un file.csv per renderlo leggibile
-
-# --- Visualizzazione dei dati ---
-
-# COVID-19
-# sns_plot = sns.countplot(x='COVID-19', data=covid)
-# figure = sns_plot.get_figure()
-# figure.savefig('COVID-19.png', dpi = 400)
-
-# # Breathing Problem 
-# sns_breathing = sns.countplot(x='Breathing Problem',hue='COVID-19',data=covid)
-# figure1 = sns_breathing.get_figure()
-# figure1.savefig('BreathingProblem.png', dpi = 400)
-
-# Fever 
-# sns_fever = sns.countplot(x='Fever', hue='COVID-19', data=covid)
-# figure2 = sns_fever.get_figure()
-# figure2.savefig('Fever.png', dpi = 400)
-
-# Dry Cough
-# sns_dry = sns.countplot(x='Dry Cough',hue='COVID-19',data=covid)
-# figure3 = sns_dry.get_figure()
-# figure3.savefig('dry.png', dpi = 400)
-
-# Sore Throat
-# sns_sore = sns.countplot(x='Sore throat',hue='COVID-19',data=covid)
-# figure4 = sns_sore.get_figure()
-# figure4.savefig('sore.png', dpi = 400)
-
-# Analisi delle accuratezze di ogni algoritmo di apprendimento
-# Accuracy LogisticRegression
-clf = LogisticRegression()
-clf.fit(X_train, Y_train)
-accuracy_logreg = clf.score(X_test, Y_test)*100
-
-# Accuracy RandomForestRegressor
-clf1 = RandomForestRegressor(max_depth = 2, n_estimators=1000)
-clf1.fit(X_train, Y_train)
-accuracy_randomforest = clf1.score(X_test, Y_test)*100
-
-# Accuracy GradientBoostingRegressor
-GBR = GradientBoostingRegressor(n_estimators=100, max_depth=4)
-GBR.fit(X_train, Y_train)
-accuracy_gbk=GBR.score(X_test, Y_test)*100
-
-# Accuracy KNeighborsClassifier
-knn = KNeighborsClassifier(n_neighbors=20)
-knn.fit(X_train, Y_train)
-y_pred = knn.predict(X_test)
-#Score/Accuracy
-accuracy_knn=knn.score(X_test, Y_test)*100
-
-# Accuracy DecisionTreeClassifier
-tree = tree.DecisionTreeClassifier()
-tree.fit(X_train,Y_train)
-y_pred1 = tree.predict(X_test)
-accuracy_decisiontree=tree.score(X_test, Y_test)*100
-
-# Accuracy Naive_bayes
-model = GaussianNB()
-model.fit(X_train,Y_train)
-accuracy_gaussian= model.score(X_test, Y_test)*100
-    
-# Classifica del miglior modello in funzione dello Score/Accuracy
-models = pd.DataFrame({
-   'Model': ['KNN', 'Logistic Regression', 
-             'Random Forest', 'Naive Bayes',   
-             'Decision Tree', 'Gradient Boosting Classifier'],
-   'Score': [accuracy_knn, accuracy_logreg, 
-             accuracy_randomforest, accuracy_gaussian, accuracy_decisiontree,
-             accuracy_gbk]})
-print(models.sort_values(by='Score', ascending=False))
-
-
-
+# Librerie utili per l'analisi dei dati
+import pandas as pd
+import numpy as np
+import matplotlib
+import seaborn as sns
+import matplotlib.pyplot as plt
+from numpy.random.mtrand import seed
+
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn import tree
+
+# Configurazione dello stile dei grafici
+sns.set(context='notebook', style='darkgrid', palette='colorblind', font='sans-serif', font_scale=1, rc=None)
+matplotlib.rcParams['figure.figsize'] =[8,8]
+matplotlib.rcParams.update({'font.size': 15})
+matplotlib.rcParams['font.family'] = 'sans-serif'
+
+# --- Preparazione dei dati ---
+# Caricamento del dataset
+
+def data_split(data, ratio):
+    np.random.seed(42)
+    shuffled = np.random.permutation(len(data))
+    test_set_size = int(len(data) * ratio)
+    test_indices = shuffled[:test_set_size]
+    train_indices = shuffled[test_set_size:]
+    return data.iloc[train_indices], data.iloc[test_indices]
+
+if __name__== "__main__":
+
+    # Read The Data
+    covid = pd.read_csv('data/data.csv')
+    train, test = data_split(covid, 0.2)
+    X_train = train[['Breathing Problem', 'Fever', 'Dry Cough', 'Sore throat',
+       'Running Nose', 'Asthma', 'Chronic Lung Disease', 'Headache',
+       'Heart Disease', 'Diabetes', 'Hyper Tension', 'Fatigue ',
+       'Gastrointestinal ', 'Abroad travel', 'Contact with COVID Patient',
+       'Attended Large Gathering', 'Visited Public Exposed Places',
+       'Family working in Public Exposed Places', 'Wearing Masks',
+       'Sanitization from Market']].to_numpy()
+
+    X_test = test[['Breathing Problem', 'Fever', 'Dry Cough', 'Sore throat',
+       'Running Nose', 'Asthma', 'Chronic Lung Disease', 'Headache',
+       'Heart Disease', 'Diabetes', 'Hyper Tension', 'Fatigue ',
+       'Gastrointestinal ', 'Abroad travel', 'Contact with COVID Patient',
+       'Attended Large Gathering', 'Visited Public Exposed Places',
+       'Family working in Public Exposed Places', 'Wearing Masks',
+       'Sanitization from Market']].to_numpy()
+
+    Y_train = train[['COVID-19']].to_numpy().reshape(4348,)
+    Y_test = test[['COVID-19']].to_numpy().reshape(1086,)
+    
+# Analisi delle accuratezze di ogni algoritmo di apprendimento
+# Accuracy LogisticRegression
+clf = LogisticRegression()
+clf.fit(X_train, Y_train)
+accuracy_logreg = clf.score(X_test, Y_test)*100
+
+# Accuracy RandomForestRegressor
+clf1 = RandomForestRegressor(max_depth = 2, n_estimators=1000)
+clf1.fit(X_train, Y_train)
+accuracy_randomforest = clf1.score(X_test, Y_test)*100
+
+# Accuracy GradientBoostingRegressor
+GBR = GradientBoostingRegressor(n_estimators=100, max_depth=4)
+GBR.fit(X_train, Y_train)
+accuracy_gbk=GBR.score(X_test, Y_test)*100
+
+# Accuracy KNeighborsClassifier
+knn = KNeighborsClassifier(n_neighbors=20)
+knn.fit(X_train, Y_train)
+y_pred = knn.predict(X_test)
+#Score/Accuracy
+accuracy_knn=knn.score(X_test, Y_test)*100
+
+# Accuracy DecisionTreeClassifier
+tree = tree.DecisionTreeClassifier()
+tree.fit(X_train,Y_train)
+y_pred1 = tree.predict(X_test)
+accuracy_decisiontree=tree.score(X_test, Y_test)*100
+
+# Accuracy Naive_bayes
+model = GaussianNB()
+model.fit(X_train,Y_train)
+accuracy_gaussian= model.score(X_test, Y_test)*100
+    
+# Classifica del miglior modello in funzione dello Score/Accuracy
+models = pd.DataFrame({
+   'Model': ['KNN', 'Logistic Regression', 
+             'Random Forest', 'Naive Bayes',   
+             'Decision Tree', 'Gradient Boosting Classifier'],
+   'Score': [accuracy_knn, accuracy_logreg, 
+             accuracy_randomforest, accuracy_gaussian, accuracy_decisiontree,
+             accuracy_gbk]})
+classifica = models.sort_values(by='Score', ascending=False)
+classifica.to_csv("models/classifica_accuracy_model")
\ No newline at end of file
diff --git a/models/classifica_accuracy_model b/models/classifica_accuracy_model
new file mode 100644
index 0000000..e0f9471
--- /dev/null
+++ b/models/classifica_accuracy_model
@@ -0,0 +1,7 @@
+,Model,Score
+4,Decision Tree,98.43462246777163
+1,Logistic Regression,97.23756906077348
+0,KNN,96.22467771639043
+5,Gradient Boosting Classifier,89.57274807620892
+3,Naive Bayes,76.61141804788214
+2,Random Forest,48.18992995680963
diff --git a/models/confusion_matrix_for_test.png b/models/confusion_matrix_for_test.png
new file mode 100644
index 0000000..d9e7414
Binary files /dev/null and b/models/confusion_matrix_for_test.png differ
diff --git a/myTraining.py b/myTraining.py
index e7dedf8..654aebd 100644
--- a/myTraining.py
+++ b/myTraining.py
@@ -32,7 +32,7 @@ def data_split(data, ratio):
 if __name__== "__main__":
 
     # Read The Data
-    df = pd.read_csv('data.csv')
+    df = pd.read_csv('data/data.csv')
     train, test = data_split(df, 0.2)
     X_train = train[['Breathing Problem', 'Fever', 'Dry Cough', 'Sore throat',
        'Running Nose', 'Asthma', 'Chronic Lung Disease', 'Headache',
@@ -63,19 +63,31 @@ def data_split(data, ratio):
     #Calcolo della prediction
     inputFeatures = [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0]
     infProb = clf.predict_proba([inputFeatures])[0][1]
+    with open("results/prediction_LogisticRegression.txt", 'w') as outfile:
+            outfile.write("TEST Risultato della predizione LogisticRegression\n")
+            outfile.write("Paziente X\n \
+- problema di respirazione (breathing problem)\n \
+- febbre (fever)\n \
+- fatica (fatigue)\n \
+- tosse secca(dry cough)\n \
+- malattia cardiaca (heart disease)\n \
+- ipertensione (hyper tension)\n \
+ha come probabilita' di infezione: \n")
+            outfile.write(str(infProb))
     print(infProb)
 
     # matrice di confusione
     tn, fp, fn, tp = confusion_matrix(Y_test, y_pred).ravel()
     # plot_confusion_matrix(clf, X_test, Y_test)
-    # plt.show()
+    # plt.savefig("models/confusion_matrix_for_test.png")
     
+    # calcoli
     accuracy_logreg = clf.score(X_test, Y_test)
     precision = precision_score(Y_test, y_pred)
     specificity = tn / (tn + fp)
     sensitivity = tp / (tp + fn)
     
-    with open("metrics.json", 'w') as outfile:
+    with open("results/metrics.json", 'w') as outfile:
         json.dump({ "accuracy": accuracy_logreg, "specificity": specificity, "sensitivity":sensitivity, "precision": precision}, outfile)
 
     # Report test set score
@@ -85,15 +97,15 @@ def data_split(data, ratio):
     print(test_score)
 
     # Scrittura dei scores al file
-    with open("score.txt", 'w') as outfile:
+    with open("results/score_monitoring.txt", 'w') as outfile:
             outfile.write("Training variance explained: %2.1f%%\n" % train_score)
             outfile.write("Test variance explained: %2.1f%%\n" % test_score)
 
     # Visualizzatore
     visualizer_report = DiscriminationThreshold(clf)
-    visualizer_report.fit(X_train, Y_train)  
+    visualizer_report.fit(X_train, Y_train) 
     visualizer_report.score(X_test, Y_test)  
-    visualizer_report.show("report.png")     
+    visualizer_report.show("results/report_threshold.png")
   
    # ----------------------------------------------------------------------------
     # open a file, where yu want to store the data
diff --git a/metrics.json b/results/metrics.json
similarity index 100%
rename from metrics.json
rename to results/metrics.json
diff --git a/results/prediction_LogisticRegression.txt b/results/prediction_LogisticRegression.txt
new file mode 100644
index 0000000..f976a57
--- /dev/null
+++ b/results/prediction_LogisticRegression.txt
@@ -0,0 +1,10 @@
+TEST Risultato della predizione LogisticRegression
+Paziente X
+ - problema di respirazione (breathing problem)
+ - febbre (fever)
+ - fatica (fatigue)
+ - tosse secca(dry cough)
+ - malattia cardiaca (heart disease)
+ - ipertensione (hyper tension)
+ ha come probabilita' di infezione: 
+0.6512933809962855
\ No newline at end of file
diff --git a/report.png b/results/report_threshold.png
similarity index 100%
rename from report.png
rename to results/report_threshold.png
diff --git a/score.txt b/results/score_monitoring.txt
similarity index 100%
rename from score.txt
rename to results/score_monitoring.txt