diff --git a/spam_hypot/best_model_and_plots.py b/spam_hypot/best_model_and_plots.py
index 2d43014..5f5ac00 100644
--- a/spam_hypot/best_model_and_plots.py
+++ b/spam_hypot/best_model_and_plots.py
@@ -5,13 +5,6 @@ import numpy as np
 import pandas as pd
 import seaborn as sns
 import matplotlib.pyplot as plt
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler, OneHotEncoder
-from sklearn.compose import ColumnTransformer
-from sklearn.pipeline import Pipeline
-from sklearn.impute import SimpleImputer
-from sklearn.ensemble import GradientBoostingClassifier
-from sklearn.metrics import roc_auc_score
 
 sns.set_theme(style="whitegrid")
 plt.rcParams["figure.figsize"] = (10, 5)
@@ -54,43 +47,6 @@ client["order_rate"] = eda.safe_divide(client["orders_amt_total"], client["imp_t
 client["order_rate_pct"] = 100 * client["order_rate"]  # чтобы шкала была человеческая
 client["avg_imp_per_day"] = eda.safe_divide(client["imp_total"], client["contact_days"])
 
-# таргет: высокий orders/impressions
-client["high_or"] = (client["order_rate"] >= client["order_rate"].quantile(0.75)).astype(int)
-
-
-X = client[["avg_imp_per_day", "imp_total", "click_total", "age", "gender_cd", "device_platform_cd"]]
-X = X.copy()
-X["gender_cd"] = eda.normalize_gender(X["gender_cd"])
-X["device_platform_cd"] = eda.normalize_device(X["device_platform_cd"])
-y = client["high_or"]
-
-
-num_cols = ["avg_imp_per_day", "imp_total", "click_total", "age"]
-cat_cols = ["gender_cd", "device_platform_cd"]
-pre = ColumnTransformer([
-    ("num", Pipeline([("imputer", SimpleImputer(strategy="median")), ("scaler", StandardScaler())]), num_cols),
-    ("cat", OneHotEncoder(handle_unknown="ignore"), cat_cols),
-])
-
-model = Pipeline([("pre", pre), ("clf", GradientBoostingClassifier(random_state=42))])
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
-model.fit(X_train, y_train)
-proba = model.predict_proba(X_test)[:, 1]
-auc = roc_auc_score(y_test, proba)
-print("Best model AUC:", auc)
-
-# Probability vs avg_imp_per_day
-grid = pd.DataFrame({"avg_imp_per_day": np.linspace(client["avg_imp_per_day"].min(), client["avg_imp_per_day"].max(), 50)})
-base = client.median(numeric_only=True)
-base_gender = client["gender_cd"].mode().iat[0]
-base_device = client["device_platform_cd"].mode().iat[0]
-grid["imp_total"] = base["imp_total"]
-grid["click_total"] = base["click_total"]
-grid["age"] = base["age"]
-grid["gender_cd"] = base_gender
-grid["device_platform_cd"] = base_device
-proba_grid = model.predict_proba(grid)[:, 1]
-
 # Mean absolute orders for each exact avg_imp_per_day (no bins), sorted ascending
 stats_imp = (
     client.groupby("avg_imp_per_day", as_index=False)
@@ -138,26 +94,51 @@ stats_f["orders_smooth"] = (
     .rolling(window=w, center=True, min_periods=1)
     .mean()
 )
+# --- cost line (linear expenses) ---
+# нормируем так, чтобы масштаб был сопоставим с заказами
+c = stats_f["orders_smooth"].max() / stats_f["avg_imp_per_day"].max()
+stats_f["cost_line"] = c * stats_f["avg_imp_per_day"]
 
-plt.figure(figsize=(10, 10))
+# plot
+plt.figure(figsize=(10, 8))
 
-# raw (filtered) curve
 plt.plot(
-    stats_f["avg_imp_per_day"], stats_f["orders_mean"],
-    marker="o", linewidth=1, alpha=0.35, label="Orders (mean)"
+    stats_f["avg_imp_per_day"],
+    stats_f["orders_mean"],
+    marker="o",
+    linewidth=1,
+    alpha=0.3,
+    label="Среднее число заказов"
 )
 
-# smoothed trend (RED)
 plt.plot(
-    stats_f["avg_imp_per_day"], stats_f["orders_smooth"],
-    color="red", linewidth=2.5, label=f"Smoothed trend"
+    stats_f["avg_imp_per_day"],
+    stats_f["orders_smooth"],
+    color="red",
+    linewidth=2.5,
+    label="Сглаженный тренд заказов"
 )
 
-plt.xlabel("avg_imp_per_day")
-plt.ylabel("Orders (mean)")
-plt.title(f"Orders + smoothed trend vs avg_imp_per_day")
+plt.plot(
+    stats_f["avg_imp_per_day"],
+    stats_f["cost_line"],
+    color="black",
+    linestyle="--",
+    linewidth=2,
+    label="Линейные расходы на показы"
+)
+
+plt.xlabel("Среднее число показов в день")
+plt.ylabel("Среднее число заказов")
+plt.title("Зависимость заказов от интенсивности коммуникаций")
+
 plt.legend()
+plt.grid(alpha=0.3)
 plt.tight_layout()
 
-plt.savefig(project_root / "spam_hypot" / "orders_vs_avg_imp_per_day_filtered_smoothed.png", dpi=150)
-print("Saved orders_vs_avg_imp_per_day_filtered_smoothed.png")
+plt.savefig(
+    project_root / "spam_hypot" / "orders_vs_avg_imp_with_costs.png",
+    dpi=150
+)
+
+print("Saved orders_vs_avg_imp_with_costs.png")
diff --git a/spam_hypot/orders_vs_avg_imp_with_costs.png b/spam_hypot/orders_vs_avg_imp_with_costs.png
new file mode 100644
index 0000000..1137f15
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diff --git a/spam_hypot/orders_vs_avg_imp_without_costs.png b/spam_hypot/orders_vs_avg_imp_without_costs.png
new file mode 100644
index 0000000..e3991f6
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diff --git a/spam_hypot/orders_vs_avg_imp_without_costs_no_filter.png b/spam_hypot/orders_vs_avg_imp_without_costs_no_filter.png
new file mode 100644
index 0000000..223518e
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diff --git a/spam_hypot/orders_vs_avg_imp_without_costs_no_filter_no_dropouts.png b/spam_hypot/orders_vs_avg_imp_without_costs_no_filter_no_dropouts.png
new file mode 100644
index 0000000..7c224a2
Binary files /dev/null and b/spam_hypot/orders_vs_avg_imp_without_costs_no_filter_no_dropouts.png differ