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Simon Majorczyk
BigData
Commits
da797f58
Commit
da797f58
authored
4 months ago
by
Mohamed Sebabti
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predictsup0
parent
352d262d
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spotify-popularity-prediction-v2/recup_predict_sup0.py
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spotify-popularity-prediction-v2/recup_predict_sup0.py
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spotify-popularity-prediction-v2/recup_predict_sup0.py
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da797f58
import
numpy
as
np
import
pandas
as
pd
import
pickle
from
sklearn.preprocessing
import
StandardScaler
# 📂 Charger le dataset complet (avec les IDs)
df
=
pd
.
read_csv
(
"
data_sup_0popularity.csv
"
)
# 📂 Charger le modèle et le scaler
with
open
(
"
random_forest_model_sup0.pkl
"
,
"
rb
"
)
as
model_file
:
rf
=
pickle
.
load
(
model_file
)
with
open
(
"
scaler_sup0.pkl
"
,
"
rb
"
)
as
scaler_file
:
scaler
=
pickle
.
load
(
scaler_file
)
# ✅ Vérifier que les features attendues sont bien présentes
expected_features
=
[
'
year
'
,
'
acousticness
'
,
'
danceability
'
,
'
energy
'
,
'
explicit
'
,
'
instrumentalness
'
,
'
key
'
,
'
liveness
'
,
'
loudness
'
,
'
mode
'
,
'
speechiness
'
,
'
tempo
'
,
'
valence
'
,
'
nb_caracteres_sans_espaces
'
,
'
nb_artistes
'
,
'
featuring
'
,
'
duree_minute
'
,
'
categorie_annee
'
,
'
categorie_tempo
'
]
# ⚠️ Vérifier si des colonnes sont manquantes
missing_features
=
[
col
for
col
in
expected_features
if
col
not
in
df
.
columns
]
if
missing_features
:
raise
ValueError
(
f
"
⚠️ Il manque ces colonnes dans le dataset :
{
missing_features
}
"
)
# 📝 Garder uniquement les colonnes nécessaires (ID + Features)
df_features
=
df
[[
'
id
'
]
+
expected_features
]
# 🎯 Extraire les features
X
=
df_features
[
expected_features
]
# 📏 Appliquer le scaler sur les features
X_scaled
=
scaler
.
transform
(
X
)
# 🔮 Faire les prédictions
df_features
[
"
predictions
"
]
=
rf
.
predict
(
X_scaled
)
# 📊 Afficher les 10 premières lignes
print
(
df_features
.
head
(
10
))
# 💾 Sauvegarder le DataFrame avec les prédictions en CSV
df_features
.
to_csv
(
"
predictions_sup0.csv
"
,
index
=
False
)
print
(
"
\n
✅ Prédictions sauvegardées dans
'
predictions_sup0.csv
'
!
"
)
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